id	sid	tid	token	lemma	pos
ejpam-1375	1	1	10_xxx_akbilgic.dvi	10_xxx_akbilgic.dvi	NUM
ejpam-1375	1	2	european	european	ADJ
ejpam-1375	1	3	journal	journal	PROPN
ejpam-1375	1	4	of	of	ADP
ejpam-1375	1	5	pure	pure	ADJ
ejpam-1375	1	6	and	and	CCONJ
ejpam-1375	1	7	applied	apply	VERB
ejpam-1375	1	8	mathematics	mathematic	NOUN
ejpam-1375	1	9	vol	vol	NOUN
ejpam-1375	1	10	.	.	PROPN
ejpam-1375	1	11	4	4	NUM
ejpam-1375	1	12	,	,	PUNCT
ejpam-1375	1	13	no	no	INTJ
ejpam-1375	1	14	.	.	NOUN
ejpam-1375	1	15	4	4	NUM
ejpam-1375	1	16	,	,	PUNCT
ejpam-1375	1	17	2011	2011	NUM
ejpam-1375	1	18	,	,	PUNCT
ejpam-1375	1	19	467	467	NUM
ejpam-1375	1	20	-	-	SYM
ejpam-1375	1	21	485	485	NUM
ejpam-1375	1	22	issn	issn	PROPN
ejpam-1375	1	23	1307	1307	NUM
ejpam-1375	1	24	-	-	SYM
ejpam-1375	1	25	5543	5543	NUM
ejpam-1375	1	26	–	–	PUNCT
ejpam-1375	1	27	www.ejpam.com	www.ejpam.com	X
ejpam-1375	1	28	predictive	predictive	ADJ
ejpam-1375	1	29	subset	subset	NOUN
ejpam-1375	1	30	selection	selection	NOUN
ejpam-1375	1	31	using	use	VERB
ejpam-1375	1	32	regression	regression	NOUN
ejpam-1375	1	33	trees	tree	NOUN
ejpam-1375	1	34	and	and	CCONJ
ejpam-1375	1	35	rbf	rbf	PROPN
ejpam-1375	1	36	neural	neural	PROPN
ejpam-1375	1	37	networks	network	NOUN
ejpam-1375	1	38	hybridized	hybridize	VERB
ejpam-1375	1	39	with	with	ADP
ejpam-1375	1	40	the	the	DET
ejpam-1375	1	41	genetic	genetic	ADJ
ejpam-1375	1	42	algorithm	algorithm	NOUN
ejpam-1375	1	43	oguz	oguz	PROPN
ejpam-1375	1	44	akbilgic1,∗	akbilgic1,∗	PROPN
ejpam-1375	1	45	,	,	PUNCT
ejpam-1375	1	46	hamparsum	hamparsum	ADJ
ejpam-1375	1	47	bozdogan2	bozdogan2	PROPN
ejpam-1375	1	48	1	1	NUM
ejpam-1375	1	49	department	department	NOUN
ejpam-1375	1	50	of	of	ADP
ejpam-1375	1	51	quantitative	quantitative	ADJ
ejpam-1375	1	52	methods	method	NOUN
ejpam-1375	1	53	,	,	PUNCT
ejpam-1375	1	54	istanbul	istanbul	PROPN
ejpam-1375	1	55	university	university	PROPN
ejpam-1375	1	56	school	school	NOUN
ejpam-1375	1	57	of	of	ADP
ejpam-1375	1	58	business	business	PROPN
ejpam-1375	1	59	administration	administration	PROPN
ejpam-1375	1	60	,	,	PUNCT
ejpam-1375	1	61	istanbul	istanbul	PROPN
ejpam-1375	1	62	,	,	PUNCT
ejpam-1375	1	63	turkey	turkey	PROPN
ejpam-1375	1	64	2	2	NUM
ejpam-1375	1	65	department	department	NOUN
ejpam-1375	1	66	of	of	ADP
ejpam-1375	1	67	statistics	statistic	NOUN
ejpam-1375	1	68	,	,	PUNCT
ejpam-1375	1	69	operations	operation	NOUN
ejpam-1375	1	70	,	,	PUNCT
ejpam-1375	1	71	and	and	CCONJ
ejpam-1375	1	72	management	management	NOUN
ejpam-1375	1	73	science	science	NOUN
ejpam-1375	1	74	,	,	PUNCT
ejpam-1375	1	75	and	and	CCONJ
ejpam-1375	1	76	center	center	NOUN
ejpam-1375	1	77	for	for	ADP
ejpam-1375	1	78	intelligent	intelligent	ADJ
ejpam-1375	1	79	systems	system	NOUN
ejpam-1375	1	80	and	and	CCONJ
ejpam-1375	1	81	machine	machine	NOUN
ejpam-1375	1	82	learning	learning	NOUN
ejpam-1375	1	83	(	(	PUNCT
ejpam-1375	1	84	cisml	cisml	PROPN
ejpam-1375	1	85	)	)	PUNCT
ejpam-1375	1	86	,	,	PUNCT
ejpam-1375	1	87	the	the	DET
ejpam-1375	1	88	university	university	PROPN
ejpam-1375	1	89	of	of	ADP
ejpam-1375	1	90	tennessee	tennessee	PROPN
ejpam-1375	1	91	,	,	PUNCT
ejpam-1375	1	92	knoxville	knoxville	PROPN
ejpam-1375	1	93	,	,	PUNCT
ejpam-1375	1	94	37996	37996	NUM
ejpam-1375	1	95	,	,	PUNCT
ejpam-1375	1	96	usa	usa	PROPN
ejpam-1375	1	97	abstract	abstract	NOUN
ejpam-1375	1	98	.	.	PUNCT
ejpam-1375	2	1	in	in	ADP
ejpam-1375	2	2	this	this	DET
ejpam-1375	2	3	paper	paper	NOUN
ejpam-1375	2	4	we	we	PRON
ejpam-1375	2	5	develop	develop	VERB
ejpam-1375	2	6	a	a	DET
ejpam-1375	2	7	novel	novel	ADJ
ejpam-1375	2	8	nonparametric	nonparametric	NOUN
ejpam-1375	2	9	predictive	predictive	NOUN
ejpam-1375	2	10	subset	subset	NOUN
ejpam-1375	2	11	regression	regression	NOUN
ejpam-1375	2	12	modeling	modeling	NOUN
ejpam-1375	2	13	procedure	procedure	NOUN
ejpam-1375	2	14	that	that	PRON
ejpam-1375	2	15	involves	involve	VERB
ejpam-1375	2	16	a	a	DET
ejpam-1375	2	17	combination	combination	NOUN
ejpam-1375	2	18	of	of	ADP
ejpam-1375	2	19	regression	regression	NOUN
ejpam-1375	2	20	trees	tree	NOUN
ejpam-1375	2	21	with	with	ADP
ejpam-1375	2	22	radial	radial	ADJ
ejpam-1375	2	23	basis	basis	NOUN
ejpam-1375	2	24	function	function	NOUN
ejpam-1375	2	25	(	(	PUNCT
ejpam-1375	2	26	rbf	rbf	PROPN
ejpam-1375	2	27	)	)	PUNCT
ejpam-1375	2	28	neural	neural	ADJ
ejpam-1375	2	29	networks	network	NOUN
ejpam-1375	2	30	hybridized	hybridize	VERB
ejpam-1375	2	31	with	with	ADP
ejpam-1375	2	32	the	the	DET
ejpam-1375	2	33	genetic	genetic	ADJ
ejpam-1375	2	34	algorithm	algorithm	NOUN
ejpam-1375	2	35	(	(	PUNCT
ejpam-1375	2	36	ga	ga	NOUN
ejpam-1375	2	37	)	)	PUNCT
ejpam-1375	2	38	to	to	PART
ejpam-1375	2	39	carry	carry	VERB
ejpam-1375	2	40	out	out	ADP
ejpam-1375	2	41	the	the	DET
ejpam-1375	2	42	subset	subset	ADJ
ejpam-1375	2	43	selection	selection	NOUN
ejpam-1375	2	44	of	of	ADP
ejpam-1375	2	45	the	the	DET
ejpam-1375	2	46	best	good	ADJ
ejpam-1375	2	47	predictors	predictor	NOUN
ejpam-1375	2	48	.	.	PUNCT
ejpam-1375	3	1	we	we	PRON
ejpam-1375	3	2	use	use	VERB
ejpam-1375	3	3	the	the	DET
ejpam-1375	3	4	information	information	NOUN
ejpam-1375	3	5	-	-	PUNCT
ejpam-1375	3	6	theoretic	theoretic	NOUN
ejpam-1375	3	7	measure	measure	NOUN
ejpam-1375	3	8	of	of	ADP
ejpam-1375	3	9	complexity	complexity	NOUN
ejpam-1375	3	10	(	(	PUNCT
ejpam-1375	3	11	icomp	icomp	ADJ
ejpam-1375	3	12	)	)	PUNCT
ejpam-1375	3	13	criterion	criterion	NOUN
ejpam-1375	3	14	of	of	ADP
ejpam-1375	3	15	[	[	X
ejpam-1375	3	16	5	5	NUM
ejpam-1375	3	17	,	,	PUNCT
ejpam-1375	3	18	6	6	NUM
ejpam-1375	3	19	,	,	PUNCT
ejpam-1375	3	20	7	7	NUM
ejpam-1375	3	21	,	,	PUNCT
ejpam-1375	3	22	8	8	NUM
ejpam-1375	3	23	]	]	PUNCT
ejpam-1375	3	24	as	as	SCONJ
ejpam-1375	3	25	our	our	PRON
ejpam-1375	3	26	fitness	fitness	NOUN
ejpam-1375	3	27	function	function	NOUN
ejpam-1375	3	28	to	to	PART
ejpam-1375	3	29	choose	choose	VERB
ejpam-1375	3	30	the	the	DET
ejpam-1375	3	31	best	good	ADJ
ejpam-1375	3	32	approximating	approximate	VERB
ejpam-1375	3	33	radial	radial	ADJ
ejpam-1375	3	34	basis	basis	NOUN
ejpam-1375	3	35	functions	function	NOUN
ejpam-1375	3	36	and	and	CCONJ
ejpam-1375	3	37	to	to	PART
ejpam-1375	3	38	choose	choose	VERB
ejpam-1375	3	39	the	the	DET
ejpam-1375	3	40	best	good	ADJ
ejpam-1375	3	41	subset	subset	NOUN
ejpam-1375	3	42	of	of	ADP
ejpam-1375	3	43	predictors	predictor	NOUN
ejpam-1375	3	44	with	with	ADP
ejpam-1375	3	45	the	the	DET
ejpam-1375	3	46	ga	ga	PROPN
ejpam-1375	3	47	.	.	PUNCT
ejpam-1375	3	48	to	to	PART
ejpam-1375	3	49	avoid	avoid	VERB
ejpam-1375	3	50	the	the	DET
ejpam-1375	3	51	potential	potential	ADJ
ejpam-1375	3	52	singularities	singularity	NOUN
ejpam-1375	3	53	in	in	ADP
ejpam-1375	3	54	the	the	DET
ejpam-1375	3	55	design	design	NOUN
ejpam-1375	3	56	matrix	matrix	NOUN
ejpam-1375	3	57	,	,	PUNCT
ejpam-1375	3	58	we	we	PRON
ejpam-1375	3	59	combine	combine	VERB
ejpam-1375	3	60	our	our	PRON
ejpam-1375	3	61	model	model	NOUN
ejpam-1375	3	62	with	with	ADP
ejpam-1375	3	63	analytical	analytical	ADJ
ejpam-1375	3	64	global	global	PROPN
ejpam-1375	3	65	ridge	ridge	PROPN
ejpam-1375	3	66	regression	regression	PROPN
ejpam-1375	3	67	for	for	ADP
ejpam-1375	3	68	regularization	regularization	NOUN
ejpam-1375	3	69	.	.	PUNCT
ejpam-1375	4	1	on	on	ADP
ejpam-1375	4	2	the	the	DET
ejpam-1375	4	3	other	other	ADJ
ejpam-1375	4	4	hand	hand	NOUN
ejpam-1375	4	5	,	,	PUNCT
ejpam-1375	4	6	estimation	estimation	NOUN
ejpam-1375	4	7	and	and	CCONJ
ejpam-1375	4	8	prediction	prediction	NOUN
ejpam-1375	4	9	performance	performance	NOUN
ejpam-1375	4	10	of	of	ADP
ejpam-1375	4	11	model	model	NOUN
ejpam-1375	4	12	also	also	ADV
ejpam-1375	4	13	taken	take	VERB
ejpam-1375	4	14	into	into	ADP
ejpam-1375	4	15	account	account	NOUN
ejpam-1375	4	16	for	for	ADP
ejpam-1375	4	17	best	good	ADJ
ejpam-1375	4	18	subset	subset	VERB
ejpam-1375	4	19	chosen	choose	VERB
ejpam-1375	4	20	.	.	PUNCT
ejpam-1375	5	1	2000	2000	NUM
ejpam-1375	5	2	mathematics	mathematic	NOUN
ejpam-1375	5	3	subject	subject	NOUN
ejpam-1375	5	4	classifications	classification	NOUN
ejpam-1375	5	5	:	:	PUNCT
ejpam-1375	5	6	62g08	62g08	NUM
ejpam-1375	5	7	;	;	PUNCT
ejpam-1375	5	8	62j02	62j02	NUM
ejpam-1375	5	9	;	;	PUNCT
ejpam-1375	5	10	17	17	NUM
ejpam-1375	5	11	-	-	SYM
ejpam-1375	5	12	08	08	NUM
ejpam-1375	5	13	;	;	PUNCT
ejpam-1375	5	14	62b10	62b10	NUM
ejpam-1375	5	15	;	;	PUNCT
ejpam-1375	5	16	62	62	NUM
ejpam-1375	5	17	-	-	SYM
ejpam-1375	5	18	07	07	NUM
ejpam-1375	5	19	key	key	ADJ
ejpam-1375	5	20	words	word	NOUN
ejpam-1375	5	21	and	and	CCONJ
ejpam-1375	5	22	phrases	phrase	NOUN
ejpam-1375	5	23	:	:	PUNCT
ejpam-1375	5	24	model	model	NOUN
ejpam-1375	5	25	selection	selection	NOUN
ejpam-1375	5	26	,	,	PUNCT
ejpam-1375	5	27	subset	subset	NOUN
ejpam-1375	5	28	selection	selection	NOUN
ejpam-1375	5	29	,	,	PUNCT
ejpam-1375	5	30	information	information	NOUN
ejpam-1375	5	31	criteria	criterion	NOUN
ejpam-1375	5	32	,	,	PUNCT
ejpam-1375	5	33	radial	radial	ADJ
ejpam-1375	5	34	basis	basis	NOUN
ejpam-1375	5	35	functions	function	NOUN
ejpam-1375	5	36	,	,	PUNCT
ejpam-1375	5	37	neural	neural	ADJ
ejpam-1375	5	38	networks	network	NOUN
ejpam-1375	5	39	1	1	NUM
ejpam-1375	5	40	.	.	PUNCT
ejpam-1375	6	1	introduction	introduction	NOUN
ejpam-1375	6	2	high	high	ADJ
ejpam-1375	6	3	dimensionality	dimensionality	NOUN
ejpam-1375	6	4	of	of	ADP
ejpam-1375	6	5	the	the	DET
ejpam-1375	6	6	independent	independent	ADJ
ejpam-1375	6	7	or	or	CCONJ
ejpam-1375	6	8	the	the	DET
ejpam-1375	6	9	predictor	predictor	NOUN
ejpam-1375	6	10	variables	variable	NOUN
ejpam-1375	6	11	in	in	ADP
ejpam-1375	6	12	regression	regression	NOUN
ejpam-1375	6	13	models	model	NOUN
ejpam-1375	6	14	increases	increase	VERB
ejpam-1375	6	15	the	the	DET
ejpam-1375	6	16	model	model	NOUN
ejpam-1375	6	17	complexity	complexity	NOUN
ejpam-1375	6	18	and	and	CCONJ
ejpam-1375	6	19	that	that	PRON
ejpam-1375	6	20	makes	make	VERB
ejpam-1375	6	21	the	the	DET
ejpam-1375	6	22	analysis	analysis	NOUN
ejpam-1375	6	23	difficult	difficult	ADJ
ejpam-1375	6	24	.	.	PUNCT
ejpam-1375	7	1	data	datum	NOUN
ejpam-1375	7	2	mining	mining	NOUN
ejpam-1375	7	3	techniques	technique	NOUN
ejpam-1375	7	4	help	help	VERB
ejpam-1375	7	5	practitioners	practitioner	NOUN
ejpam-1375	7	6	to	to	PART
ejpam-1375	7	7	overcome	overcome	VERB
ejpam-1375	7	8	such	such	ADJ
ejpam-1375	7	9	problems	problem	NOUN
ejpam-1375	7	10	.	.	PUNCT
ejpam-1375	8	1	in	in	ADP
ejpam-1375	8	2	this	this	DET
ejpam-1375	8	3	frame	frame	NOUN
ejpam-1375	8	4	work	work	NOUN
ejpam-1375	8	5	,	,	PUNCT
ejpam-1375	8	6	model	model	NOUN
ejpam-1375	8	7	selection	selection	NOUN
ejpam-1375	8	8	is	be	AUX
ejpam-1375	8	9	an	an	DET
ejpam-1375	8	10	important	important	ADJ
ejpam-1375	8	11	tool	tool	NOUN
ejpam-1375	8	12	to	to	PART
ejpam-1375	8	13	reduce	reduce	VERB
ejpam-1375	8	14	the	the	DET
ejpam-1375	8	15	dimensionality	dimensionality	NOUN
ejpam-1375	8	16	and	and	CCONJ
ejpam-1375	8	17	to	to	PART
ejpam-1375	8	18	measure	measure	VERB
ejpam-1375	8	19	the	the	DET
ejpam-1375	8	20	model	model	NOUN
ejpam-1375	8	21	complexity	complexity	NOUN
ejpam-1375	8	22	is	be	AUX
ejpam-1375	8	23	an	an	DET
ejpam-1375	8	24	an	an	DET
ejpam-1375	8	25	important	important	ADJ
ejpam-1375	8	26	enterprize	enterprize	NOUN
ejpam-1375	8	27	to	to	PART
ejpam-1375	8	28	find	find	VERB
ejpam-1375	8	29	a	a	DET
ejpam-1375	8	30	subset	subset	NOUN
ejpam-1375	8	31	of	of	ADP
ejpam-1375	8	32	predictor	predictor	NOUN
ejpam-1375	8	33	variables	variable	NOUN
ejpam-1375	8	34	which	which	PRON
ejpam-1375	8	35	represent	represent	VERB
ejpam-1375	8	36	the	the	DET
ejpam-1375	8	37	underlying	underlie	VERB
ejpam-1375	8	38	relationship	relationship	NOUN
ejpam-1375	8	39	between	between	ADP
ejpam-1375	8	40	the	the	DET
ejpam-1375	8	41	input	input	NOUN
ejpam-1375	8	42	or	or	CCONJ
ejpam-1375	8	43	predictor	predictor	NOUN
ejpam-1375	8	44	and	and	CCONJ
ejpam-1375	8	45	output	output	NOUN
ejpam-1375	8	46	or	or	CCONJ
ejpam-1375	8	47	response	response	NOUN
ejpam-1375	8	48	variables	variable	NOUN
ejpam-1375	8	49	.	.	PUNCT
ejpam-1375	9	1	although	although	SCONJ
ejpam-1375	9	2	in	in	ADP
ejpam-1375	9	3	the	the	DET
ejpam-1375	9	4	literature	literature	NOUN
ejpam-1375	9	5	there	there	PRON
ejpam-1375	9	6	are	be	VERB
ejpam-1375	9	7	many	many	ADJ
ejpam-1375	9	8	different	different	ADJ
ejpam-1375	9	9	model	model	NOUN
ejpam-1375	9	10	selection	selection	NOUN
ejpam-1375	9	11	criteria	criterion	NOUN
ejpam-1375	9	12	that	that	PRON
ejpam-1375	9	13	have	have	AUX
ejpam-1375	9	14	been	be	AUX
ejpam-1375	9	15	proposed	propose	VERB
ejpam-1375	9	16	and	and	CCONJ
ejpam-1375	9	17	used	use	VERB
ejpam-1375	9	18	,	,	PUNCT
ejpam-1375	9	19	most	most	ADJ
ejpam-1375	9	20	of	of	ADP
ejpam-1375	9	21	these	these	DET
ejpam-1375	9	22	criteria	criterion	NOUN
ejpam-1375	9	23	are	be	AUX
ejpam-1375	9	24	based	base	VERB
ejpam-1375	9	25	on	on	ADP
ejpam-1375	9	26	akaike	akaike	PROPN
ejpam-1375	9	27	’s	’s	PART
ejpam-1375	9	28	information	information	NOUN
ejpam-1375	9	29	criterion	criterion	NOUN
ejpam-1375	9	30	(	(	PUNCT
ejpam-1375	9	31	aic	aic	PROPN
ejpam-1375	9	32	)	)	PUNCT
ejpam-1375	9	33	,	,	PUNCT
ejpam-1375	9	34	or	or	CCONJ
ejpam-1375	9	35	they	they	PRON
ejpam-1375	9	36	are	be	AUX
ejpam-1375	9	37	based	base	VERB
ejpam-1375	9	38	on	on	ADP
ejpam-1375	9	39	some	some	DET
ejpam-1375	9	40	variations	variation	NOUN
ejpam-1375	9	41	of	of	ADP
ejpam-1375	9	42	aic	aic	PROPN
ejpam-1375	9	43	.	.	PUNCT
ejpam-1375	10	1	in	in	ADP
ejpam-1375	10	2	contrast	contrast	NOUN
ejpam-1375	10	3	to	to	ADP
ejpam-1375	10	4	aic	aic	PROPN
ejpam-1375	10	5	,	,	PUNCT
ejpam-1375	10	6	information	information	NOUN
ejpam-1375	10	7	complexity	complexity	NOUN
ejpam-1375	10	8	(	(	PUNCT
ejpam-1375	10	9	icom	icom	PROPN
ejpam-1375	10	10	p	p	NOUN
ejpam-1375	10	11	)	)	PUNCT
ejpam-1375	10	12	type	type	NOUN
ejpam-1375	10	13	criteria	criterion	NOUN
ejpam-1375	10	14	constitute	constitute	VERB
ejpam-1375	10	15	a	a	DET
ejpam-1375	10	16	new	new	ADJ
ejpam-1375	10	17	class	class	NOUN
ejpam-1375	10	18	or	or	CCONJ
ejpam-1375	10	19	a	a	DET
ejpam-1375	10	20	new	new	ADJ
ejpam-1375	10	21	generation	generation	NOUN
ejpam-1375	10	22	model	model	NOUN
ejpam-1375	10	23	selection	selection	NOUN
ejpam-1375	10	24	criteria	criterion	NOUN
ejpam-1375	10	25	.	.	PUNCT
ejpam-1375	11	1	∗corresponding	∗corresponde	VERB
ejpam-1375	11	2	author	author	NOUN
ejpam-1375	11	3	.	.	PUNCT
ejpam-1375	12	1	email	email	NOUN
ejpam-1375	12	2	addresses	address	NOUN
ejpam-1375	12	3	:	:	PUNCT
ejpam-1375	12	4	oguzakbilgi	oguzakbilgi	PROPN
ejpam-1375	12	5	�	�	PROPN
ejpam-1375	12	6	gmail	gmail	NOUN
ejpam-1375	12	7	.	.	PUNCT
ejpam-1375	13	1	om	om	PROPN
ejpam-1375	13	2	(	(	PUNCT
ejpam-1375	13	3	o.	o.	PROPN
ejpam-1375	13	4	akbilgic	akbilgic	PROPN
ejpam-1375	13	5	)	)	PUNCT
ejpam-1375	13	6	,	,	PUNCT
ejpam-1375	13	7	bozdogan�utk.edu	bozdogan�utk.edu	PROPN
ejpam-1375	13	8	(	(	PUNCT
ejpam-1375	13	9	h.	h.	PROPN
ejpam-1375	13	10	bozdogan	bozdogan	PROPN
ejpam-1375	13	11	)	)	PUNCT
ejpam-1375	13	12	http://www.ejpam.com	http://www.ejpam.com	X
ejpam-1375	14	1	467	467	NUM
ejpam-1375	14	2	c	c	X
ejpam-1375	14	3	©	©	PROPN
ejpam-1375	14	4	2011	2011	NUM
ejpam-1375	14	5	ejpam	ejpam	VERB
ejpam-1375	14	6	all	all	DET
ejpam-1375	14	7	rights	right	NOUN
ejpam-1375	14	8	reserved	reserve	VERB
ejpam-1375	14	9	.	.	PUNCT
ejpam-1375	15	1	o.	o.	PROPN
ejpam-1375	15	2	akbilgic	akbilgic	PROPN
ejpam-1375	15	3	,	,	PUNCT
ejpam-1375	15	4	h.	h.	PROPN
ejpam-1375	15	5	bozdogan	bozdogan	PROPN
ejpam-1375	15	6	/	/	SYM
ejpam-1375	15	7	eur	eur	PROPN
ejpam-1375	15	8	.	.	PUNCT
ejpam-1375	16	1	j.	j.	PROPN
ejpam-1375	16	2	pure	pure	PROPN
ejpam-1375	16	3	appl	appl	PROPN
ejpam-1375	16	4	.	.	PROPN
ejpam-1375	16	5	math	math	PROPN
ejpam-1375	16	6	,	,	PUNCT
ejpam-1375	16	7	4	4	NUM
ejpam-1375	16	8	(	(	PUNCT
ejpam-1375	16	9	2011	2011	NUM
ejpam-1375	16	10	)	)	PUNCT
ejpam-1375	16	11	,	,	PUNCT
ejpam-1375	16	12	467	467	NUM
ejpam-1375	16	13	-	-	SYM
ejpam-1375	16	14	485	485	NUM
ejpam-1375	16	15	468	468	NUM
ejpam-1375	16	16	in	in	ADP
ejpam-1375	16	17	model	model	NOUN
ejpam-1375	16	18	selection	selection	NOUN
ejpam-1375	16	19	procedures	procedure	NOUN
ejpam-1375	16	20	,	,	PUNCT
ejpam-1375	16	21	the	the	DET
ejpam-1375	16	22	chosen	choose	VERB
ejpam-1375	16	23	model	model	NOUN
ejpam-1375	16	24	is	be	AUX
ejpam-1375	16	25	important	important	ADJ
ejpam-1375	16	26	as	as	ADV
ejpam-1375	16	27	much	much	ADV
ejpam-1375	16	28	as	as	ADP
ejpam-1375	16	29	the	the	DET
ejpam-1375	16	30	chosen	choose	VERB
ejpam-1375	16	31	model	model	NOUN
ejpam-1375	16	32	selection	selection	NOUN
ejpam-1375	16	33	criterion	criterion	NOUN
ejpam-1375	16	34	.	.	PUNCT
ejpam-1375	17	1	the	the	DET
ejpam-1375	17	2	assumption	assumption	NOUN
ejpam-1375	17	3	of	of	ADP
ejpam-1375	17	4	linear	linear	PROPN
ejpam-1375	17	5	relationship	relationship	NOUN
ejpam-1375	17	6	between	between	ADP
ejpam-1375	17	7	input	input	NOUN
ejpam-1375	17	8	and	and	CCONJ
ejpam-1375	17	9	output	output	NOUN
ejpam-1375	17	10	variables	variable	NOUN
ejpam-1375	17	11	can	can	AUX
ejpam-1375	17	12	lead	lead	VERB
ejpam-1375	17	13	us	we	PRON
ejpam-1375	17	14	to	to	PART
ejpam-1375	17	15	choose	choose	VERB
ejpam-1375	17	16	wrong	wrong	ADJ
ejpam-1375	17	17	subset	subset	NOUN
ejpam-1375	17	18	of	of	ADP
ejpam-1375	17	19	variables	variable	NOUN
ejpam-1375	17	20	.	.	PUNCT
ejpam-1375	18	1	radial	radial	ADJ
ejpam-1375	18	2	basis	basis	NOUN
ejpam-1375	18	3	function	function	NOUN
ejpam-1375	18	4	neural	neural	ADJ
ejpam-1375	18	5	networks	network	NOUN
ejpam-1375	18	6	(	(	PUNCT
ejpam-1375	18	7	rbf	rbf	PROPN
ejpam-1375	18	8	-	-	PUNCT
ejpam-1375	18	9	nn	nn	NOUN
ejpam-1375	18	10	)	)	PUNCT
ejpam-1375	18	11	,	,	PUNCT
ejpam-1375	18	12	or	or	CCONJ
ejpam-1375	18	13	what	what	PRON
ejpam-1375	18	14	statisticians	statistician	NOUN
ejpam-1375	18	15	call	call	VERB
ejpam-1375	18	16	nonparametric	nonparametric	NOUN
ejpam-1375	18	17	regression	regression	NOUN
ejpam-1375	18	18	models	model	NOUN
ejpam-1375	18	19	,	,	PUNCT
ejpam-1375	18	20	seem	seem	VERB
ejpam-1375	18	21	to	to	PART
ejpam-1375	18	22	be	be	AUX
ejpam-1375	18	23	more	more	ADV
ejpam-1375	18	24	appropriate	appropriate	ADJ
ejpam-1375	18	25	in	in	ADP
ejpam-1375	18	26	general	general	ADJ
ejpam-1375	18	27	because	because	SCONJ
ejpam-1375	18	28	rbf	rbf	PROPN
ejpam-1375	18	29	-	-	PUNCT
ejpam-1375	18	30	nn	nn	PROPN
ejpam-1375	18	31	does	do	AUX
ejpam-1375	18	32	not	not	PART
ejpam-1375	18	33	assume	assume	VERB
ejpam-1375	18	34	any	any	DET
ejpam-1375	18	35	functional	functional	ADJ
ejpam-1375	18	36	relation	relation	NOUN
ejpam-1375	18	37	between	between	ADP
ejpam-1375	18	38	input	input	NOUN
ejpam-1375	18	39	and	and	CCONJ
ejpam-1375	18	40	output	output	NOUN
ejpam-1375	18	41	variables	variable	NOUN
ejpam-1375	18	42	.	.	PUNCT
ejpam-1375	19	1	therefore	therefore	ADV
ejpam-1375	19	2	,	,	PUNCT
ejpam-1375	19	3	combining	combine	VERB
ejpam-1375	19	4	rbf	rbf	PROPN
ejpam-1375	19	5	-	-	PUNCT
ejpam-1375	19	6	nn	nn	PROPN
ejpam-1375	19	7	with	with	ADP
ejpam-1375	19	8	some	some	DET
ejpam-1375	19	9	statistical	statistical	ADJ
ejpam-1375	19	10	techniques	technique	NOUN
ejpam-1375	19	11	can	can	AUX
ejpam-1375	19	12	provide	provide	VERB
ejpam-1375	19	13	us	we	PRON
ejpam-1375	19	14	better	well	ADJ
ejpam-1375	19	15	results	result	NOUN
ejpam-1375	19	16	and	and	CCONJ
ejpam-1375	19	17	improve	improve	VERB
ejpam-1375	19	18	the	the	DET
ejpam-1375	19	19	prediction	prediction	NOUN
ejpam-1375	19	20	accuracy	accuracy	NOUN
ejpam-1375	19	21	in	in	ADP
ejpam-1375	19	22	regression	regression	NOUN
ejpam-1375	19	23	modeling	modeling	NOUN
ejpam-1375	19	24	.	.	PUNCT
ejpam-1375	20	1	the	the	DET
ejpam-1375	20	2	idea	idea	NOUN
ejpam-1375	20	3	of	of	ADP
ejpam-1375	20	4	combining	combine	VERB
ejpam-1375	20	5	rbf	rbf	PROPN
ejpam-1375	20	6	-	-	PUNCT
ejpam-1375	20	7	nn	nn	PROPN
ejpam-1375	20	8	and	and	CCONJ
ejpam-1375	20	9	regression	regression	NOUN
ejpam-1375	20	10	trees	tree	NOUN
ejpam-1375	20	11	(	(	PUNCT
ejpam-1375	20	12	rt	rt	PROPN
ejpam-1375	20	13	)	)	PUNCT
ejpam-1375	20	14	goes	go	VERB
ejpam-1375	20	15	back	back	ADV
ejpam-1375	20	16	to	to	ADP
ejpam-1375	20	17	[	[	X
ejpam-1375	20	18	12	12	NUM
ejpam-1375	20	19	]	]	PUNCT
ejpam-1375	20	20	where	where	SCONJ
ejpam-1375	20	21	how	how	SCONJ
ejpam-1375	20	22	to	to	PART
ejpam-1375	20	23	combine	combine	VERB
ejpam-1375	20	24	rbf	rbf	PROPN
ejpam-1375	20	25	-	-	PUNCT
ejpam-1375	20	26	nn	nn	PROPN
ejpam-1375	20	27	and	and	CCONJ
ejpam-1375	20	28	decision	decision	NOUN
ejpam-1375	20	29	trees	tree	NOUN
ejpam-1375	20	30	are	be	AUX
ejpam-1375	20	31	explained	explain	VERB
ejpam-1375	20	32	.	.	PUNCT
ejpam-1375	21	1	later	later	ADV
ejpam-1375	21	2	,	,	PUNCT
ejpam-1375	21	3	[	[	X
ejpam-1375	21	4	19	19	NUM
ejpam-1375	21	5	]	]	PUNCT
ejpam-1375	21	6	extended	extend	VERB
ejpam-1375	21	7	this	this	DET
ejpam-1375	21	8	idea	idea	NOUN
ejpam-1375	21	9	to	to	PART
ejpam-1375	21	10	combine	combine	VERB
ejpam-1375	21	11	rbf	rbf	PROPN
ejpam-1375	21	12	-	-	PUNCT
ejpam-1375	21	13	nn	nn	PROPN
ejpam-1375	21	14	with	with	ADP
ejpam-1375	21	15	regression	regression	NOUN
ejpam-1375	21	16	and	and	CCONJ
ejpam-1375	21	17	classification	classification	NOUN
ejpam-1375	21	18	trees	tree	NOUN
ejpam-1375	21	19	.	.	PUNCT
ejpam-1375	22	1	based	base	VERB
ejpam-1375	22	2	on	on	ADP
ejpam-1375	22	3	the	the	DET
ejpam-1375	22	4	results	result	NOUN
ejpam-1375	22	5	of	of	ADP
ejpam-1375	22	6	[	[	X
ejpam-1375	22	7	12	12	NUM
ejpam-1375	22	8	]	]	PUNCT
ejpam-1375	22	9	and	and	CCONJ
ejpam-1375	22	10	[	[	X
ejpam-1375	22	11	19	19	NUM
ejpam-1375	22	12	]	]	PUNCT
ejpam-1375	22	13	,	,	PUNCT
ejpam-1375	22	14	in	in	ADP
ejpam-1375	22	15	this	this	DET
ejpam-1375	22	16	paper	paper	NOUN
ejpam-1375	22	17	for	for	ADP
ejpam-1375	22	18	the	the	DET
ejpam-1375	22	19	first	first	ADJ
ejpam-1375	22	20	time	time	NOUN
ejpam-1375	22	21	,	,	PUNCT
ejpam-1375	22	22	we	we	PRON
ejpam-1375	22	23	combine	combine	VERB
ejpam-1375	22	24	rbf	rbf	PROPN
ejpam-1375	22	25	-	-	PROPN
ejpam-1375	22	26	nn	nn	PROPN
ejpam-1375	22	27	and	and	CCONJ
ejpam-1375	22	28	rt	rt	PROPN
ejpam-1375	22	29	model	model	NOUN
ejpam-1375	22	30	and	and	CCONJ
ejpam-1375	22	31	we	we	PRON
ejpam-1375	22	32	hybridize	hybridize	VERB
ejpam-1375	22	33	it	it	PRON
ejpam-1375	22	34	with	with	ADP
ejpam-1375	22	35	ridge	ridge	NOUN
ejpam-1375	22	36	regression	regression	NOUN
ejpam-1375	22	37	,	,	PUNCT
ejpam-1375	22	38	to	to	PART
ejpam-1375	22	39	overcome	overcome	VERB
ejpam-1375	22	40	possible	possible	ADJ
ejpam-1375	22	41	singularity	singularity	NOUN
ejpam-1375	22	42	problem	problem	NOUN
ejpam-1375	22	43	on	on	ADP
ejpam-1375	22	44	design	design	NOUN
ejpam-1375	22	45	matrix	matrix	NOUN
ejpam-1375	22	46	.	.	PUNCT
ejpam-1375	23	1	we	we	PRON
ejpam-1375	23	2	introduce	introduce	VERB
ejpam-1375	23	3	the	the	DET
ejpam-1375	23	4	genetic	genetic	ADJ
ejpam-1375	23	5	algorithm	algorithm	NOUN
ejpam-1375	23	6	(	(	PUNCT
ejpam-1375	23	7	ga	ga	NOUN
ejpam-1375	23	8	)	)	PUNCT
ejpam-1375	23	9	to	to	PART
ejpam-1375	23	10	choose	choose	VERB
ejpam-1375	23	11	best	good	ADJ
ejpam-1375	23	12	subset	subset	NOUN
ejpam-1375	23	13	of	of	ADP
ejpam-1375	23	14	input	input	NOUN
ejpam-1375	23	15	variables	variable	NOUN
ejpam-1375	23	16	by	by	ADP
ejpam-1375	23	17	scoring	score	VERB
ejpam-1375	23	18	the	the	DET
ejpam-1375	23	19	information	information	NOUN
ejpam-1375	23	20	complexity	complexity	NOUN
ejpam-1375	23	21	(	(	PUNCT
ejpam-1375	23	22	icom	icom	PROPN
ejpam-1375	23	23	p	p	NOUN
ejpam-1375	23	24	)	)	PUNCT
ejpam-1375	23	25	criterion	criterion	NOUN
ejpam-1375	23	26	.	.	PUNCT
ejpam-1375	24	1	the	the	DET
ejpam-1375	24	2	paper	paper	NOUN
ejpam-1375	24	3	is	be	AUX
ejpam-1375	24	4	organized	organize	VERB
ejpam-1375	24	5	as	as	SCONJ
ejpam-1375	24	6	follows	follow	VERB
ejpam-1375	24	7	.	.	PUNCT
ejpam-1375	25	1	in	in	ADP
ejpam-1375	25	2	section	section	NOUN
ejpam-1375	25	3	2	2	NUM
ejpam-1375	25	4	,	,	PUNCT
ejpam-1375	25	5	we	we	PRON
ejpam-1375	25	6	present	present	VERB
ejpam-1375	25	7	linear	linear	NOUN
ejpam-1375	25	8	models	model	NOUN
ejpam-1375	25	9	and	and	CCONJ
ejpam-1375	25	10	radial	radial	ADJ
ejpam-1375	25	11	basis	basis	NOUN
ejpam-1375	25	12	function	function	NOUN
ejpam-1375	25	13	neural	neural	ADJ
ejpam-1375	25	14	networks	network	NOUN
ejpam-1375	25	15	(	(	PUNCT
ejpam-1375	25	16	rbf	rbf	PROPN
ejpam-1375	25	17	-	-	PUNCT
ejpam-1375	25	18	nn	nn	NOUN
ejpam-1375	25	19	)	)	PUNCT
ejpam-1375	25	20	with	with	ADP
ejpam-1375	25	21	least	least	ADJ
ejpam-1375	25	22	squares	square	NOUN
ejpam-1375	25	23	estimation	estimation	NOUN
ejpam-1375	25	24	.	.	PUNCT
ejpam-1375	26	1	section	section	NOUN
ejpam-1375	26	2	3	3	NUM
ejpam-1375	26	3	,	,	PUNCT
ejpam-1375	26	4	presents	present	VERB
ejpam-1375	26	5	combination	combination	NOUN
ejpam-1375	26	6	of	of	ADP
ejpam-1375	26	7	regression	regression	NOUN
ejpam-1375	26	8	trees	tree	NOUN
ejpam-1375	26	9	and	and	CCONJ
ejpam-1375	26	10	rbf	rbf	PROPN
ejpam-1375	26	11	-	-	PUNCT
ejpam-1375	26	12	nn	nn	PROPN
ejpam-1375	26	13	.	.	PROPN
ejpam-1375	26	14	in	in	ADP
ejpam-1375	26	15	this	this	DET
ejpam-1375	26	16	section	section	NOUN
ejpam-1375	26	17	we	we	PRON
ejpam-1375	26	18	discuss	discuss	VERB
ejpam-1375	26	19	how	how	SCONJ
ejpam-1375	26	20	to	to	PART
ejpam-1375	26	21	transform	transform	VERB
ejpam-1375	26	22	the	the	DET
ejpam-1375	26	23	tree	tree	NOUN
ejpam-1375	26	24	nodes	node	NOUN
ejpam-1375	26	25	into	into	ADP
ejpam-1375	26	26	rbfs	rbfs	NOUN
ejpam-1375	26	27	.	.	PUNCT
ejpam-1375	27	1	in	in	ADP
ejpam-1375	27	2	section	section	NOUN
ejpam-1375	27	3	4	4	NUM
ejpam-1375	27	4	,	,	PUNCT
ejpam-1375	27	5	we	we	PRON
ejpam-1375	27	6	present	present	VERB
ejpam-1375	27	7	subset	subset	NOUN
ejpam-1375	27	8	selection	selection	NOUN
ejpam-1375	27	9	of	of	ADP
ejpam-1375	27	10	rbfs	rbfs	NOUN
ejpam-1375	27	11	and	and	CCONJ
ejpam-1375	27	12	state	state	NOUN
ejpam-1375	27	13	the	the	DET
ejpam-1375	27	14	current	current	ADJ
ejpam-1375	27	15	problems	problem	NOUN
ejpam-1375	27	16	of	of	ADP
ejpam-1375	27	17	forward	forward	ADV
ejpam-1375	27	18	,	,	PUNCT
ejpam-1375	27	19	backward	backward	ADJ
ejpam-1375	27	20	,	,	PUNCT
ejpam-1375	27	21	combination	combination	NOUN
ejpam-1375	27	22	of	of	ADP
ejpam-1375	27	23	forward	forward	NOUN
ejpam-1375	27	24	and	and	CCONJ
ejpam-1375	27	25	backward	backward	ADJ
ejpam-1375	27	26	,	,	PUNCT
ejpam-1375	27	27	and	and	CCONJ
ejpam-1375	27	28	all	all	DET
ejpam-1375	27	29	possible	possible	ADJ
ejpam-1375	27	30	subset	subset	NOUN
ejpam-1375	27	31	selection	selection	NOUN
ejpam-1375	27	32	procedures	procedure	NOUN
ejpam-1375	27	33	currently	currently	ADV
ejpam-1375	27	34	used	use	VERB
ejpam-1375	27	35	in	in	ADP
ejpam-1375	27	36	the	the	DET
ejpam-1375	27	37	literature	literature	NOUN
ejpam-1375	27	38	.	.	PUNCT
ejpam-1375	28	1	to	to	PART
ejpam-1375	28	2	avoid	avoid	VERB
ejpam-1375	28	3	over	over	ADP
ejpam-1375	28	4	-	-	PUNCT
ejpam-1375	28	5	fitting	fit	VERB
ejpam-1375	28	6	and	and	CCONJ
ejpam-1375	28	7	the	the	DET
ejpam-1375	28	8	potential	potential	ADJ
ejpam-1375	28	9	singularities	singularity	NOUN
ejpam-1375	28	10	in	in	ADP
ejpam-1375	28	11	the	the	DET
ejpam-1375	28	12	regression	regression	NOUN
ejpam-1375	28	13	design	design	NOUN
ejpam-1375	28	14	or	or	CCONJ
ejpam-1375	28	15	model	model	NOUN
ejpam-1375	28	16	matrix	matrix	NOUN
ejpam-1375	28	17	,	,	PUNCT
ejpam-1375	28	18	in	in	ADP
ejpam-1375	28	19	section	section	NOUN
ejpam-1375	28	20	5	5	NUM
ejpam-1375	28	21	,	,	PUNCT
ejpam-1375	28	22	we	we	PRON
ejpam-1375	28	23	discuss	discuss	VERB
ejpam-1375	28	24	two	two	NUM
ejpam-1375	28	25	main	main	ADJ
ejpam-1375	28	26	ways	way	NOUN
ejpam-1375	28	27	of	of	ADP
ejpam-1375	28	28	regularization	regularization	NOUN
ejpam-1375	28	29	and	and	CCONJ
ejpam-1375	28	30	present	present	ADJ
ejpam-1375	28	31	global	global	ADJ
ejpam-1375	28	32	and	and	CCONJ
ejpam-1375	28	33	local	local	PROPN
ejpam-1375	28	34	ridge	ridge	NOUN
ejpam-1375	28	35	regression	regression	NOUN
ejpam-1375	28	36	.	.	PUNCT
ejpam-1375	29	1	we	we	PRON
ejpam-1375	29	2	provide	provide	VERB
ejpam-1375	29	3	several	several	ADJ
ejpam-1375	29	4	ways	way	NOUN
ejpam-1375	29	5	of	of	ADP
ejpam-1375	29	6	choosing	choose	VERB
ejpam-1375	29	7	optimal	optimal	ADJ
ejpam-1375	29	8	ridge	ridge	NOUN
ejpam-1375	29	9	parameters	parameter	NOUN
ejpam-1375	29	10	.	.	PUNCT
ejpam-1375	30	1	section	section	NOUN
ejpam-1375	30	2	6	6	NUM
ejpam-1375	30	3	presents	present	VERB
ejpam-1375	30	4	several	several	ADJ
ejpam-1375	30	5	information	information	NOUN
ejpam-1375	30	6	-	-	PUNCT
ejpam-1375	30	7	theoretic	theoretic	NOUN
ejpam-1375	30	8	model	model	NOUN
ejpam-1375	30	9	selection	selection	NOUN
ejpam-1375	30	10	criteria	criterion	NOUN
ejpam-1375	30	11	.	.	PUNCT
ejpam-1375	31	1	for	for	ADP
ejpam-1375	31	2	space	space	NOUN
ejpam-1375	31	3	considerations	consideration	NOUN
ejpam-1375	31	4	,	,	PUNCT
ejpam-1375	31	5	we	we	PRON
ejpam-1375	31	6	restrict	restrict	VERB
ejpam-1375	31	7	the	the	DET
ejpam-1375	31	8	detailed	detailed	ADJ
ejpam-1375	31	9	proofs	proof	NOUN
ejpam-1375	31	10	and	and	CCONJ
ejpam-1375	31	11	derivations	derivation	NOUN
ejpam-1375	31	12	of	of	ADP
ejpam-1375	31	13	these	these	DET
ejpam-1375	31	14	criteria	criterion	NOUN
ejpam-1375	31	15	where	where	SCONJ
ejpam-1375	31	16	appropriate	appropriate	ADJ
ejpam-1375	31	17	.	.	PUNCT
ejpam-1375	32	1	for	for	ADP
ejpam-1375	32	2	more	more	ADJ
ejpam-1375	32	3	details	detail	NOUN
ejpam-1375	32	4	on	on	ADP
ejpam-1375	32	5	information	information	NOUN
ejpam-1375	32	6	criteria	criterion	NOUN
ejpam-1375	32	7	,	,	PUNCT
ejpam-1375	32	8	we	we	PRON
ejpam-1375	32	9	will	will	AUX
ejpam-1375	32	10	refer	refer	VERB
ejpam-1375	32	11	the	the	DET
ejpam-1375	32	12	readers	reader	NOUN
ejpam-1375	32	13	to	to	ADP
ejpam-1375	32	14	[	[	X
ejpam-1375	32	15	5	5	NUM
ejpam-1375	32	16	,	,	PUNCT
ejpam-1375	32	17	6	6	NUM
ejpam-1375	32	18	,	,	PUNCT
ejpam-1375	32	19	7	7	NUM
ejpam-1375	32	20	,	,	PUNCT
ejpam-1375	32	21	8	8	NUM
ejpam-1375	32	22	]	]	PUNCT
ejpam-1375	32	23	.	.	PUNCT
ejpam-1375	33	1	we	we	PRON
ejpam-1375	33	2	further	far	ADV
ejpam-1375	33	3	provide	provide	VERB
ejpam-1375	33	4	the	the	DET
ejpam-1375	33	5	derived	derive	VERB
ejpam-1375	33	6	forms	form	NOUN
ejpam-1375	33	7	of	of	ADP
ejpam-1375	33	8	the	the	DET
ejpam-1375	33	9	model	model	NOUN
ejpam-1375	33	10	selection	selection	NOUN
ejpam-1375	33	11	criteria	criterion	NOUN
ejpam-1375	33	12	in	in	ADP
ejpam-1375	33	13	rbf	rbf	PROPN
ejpam-1375	33	14	-	-	PUNCT
ejpam-1375	33	15	nn	nn	PROPN
ejpam-1375	33	16	.	.	PROPN
ejpam-1375	33	17	in	in	ADP
ejpam-1375	33	18	section	section	NOUN
ejpam-1375	33	19	7	7	NUM
ejpam-1375	33	20	,	,	PUNCT
ejpam-1375	33	21	we	we	PRON
ejpam-1375	33	22	present	present	VERB
ejpam-1375	33	23	the	the	DET
ejpam-1375	33	24	general	general	ADJ
ejpam-1375	33	25	background	background	NOUN
ejpam-1375	33	26	of	of	ADP
ejpam-1375	33	27	the	the	DET
ejpam-1375	33	28	genetic	genetic	ADJ
ejpam-1375	33	29	algorithm	algorithm	NOUN
ejpam-1375	33	30	(	(	PUNCT
ejpam-1375	33	31	ga	ga	NOUN
ejpam-1375	33	32	)	)	PUNCT
ejpam-1375	33	33	and	and	CCONJ
ejpam-1375	33	34	its	its	PRON
ejpam-1375	33	35	implementation	implementation	NOUN
ejpam-1375	33	36	within	within	ADP
ejpam-1375	33	37	the	the	DET
ejpam-1375	33	38	rbf	rbf	PROPN
ejpam-1375	33	39	-	-	PUNCT
ejpam-1375	33	40	nn	nn	PROPN
ejpam-1375	33	41	.	.	PROPN
ejpam-1375	33	42	in	in	ADP
ejpam-1375	33	43	section	section	NOUN
ejpam-1375	33	44	8	8	NUM
ejpam-1375	33	45	,	,	PUNCT
ejpam-1375	33	46	we	we	PRON
ejpam-1375	33	47	provide	provide	VERB
ejpam-1375	33	48	a	a	DET
ejpam-1375	33	49	large	large	ADJ
ejpam-1375	33	50	scale	scale	NOUN
ejpam-1375	33	51	simulation	simulation	NOUN
ejpam-1375	33	52	study	study	NOUN
ejpam-1375	33	53	using	use	VERB
ejpam-1375	33	54	a	a	DET
ejpam-1375	33	55	highly	highly	ADV
ejpam-1375	33	56	nonlinear	nonlinear	ADJ
ejpam-1375	33	57	simulation	simulation	NOUN
ejpam-1375	33	58	protocol	protocol	NOUN
ejpam-1375	33	59	where	where	SCONJ
ejpam-1375	33	60	we	we	PRON
ejpam-1375	33	61	first	first	ADV
ejpam-1375	33	62	choose	choose	VERB
ejpam-1375	33	63	the	the	DET
ejpam-1375	33	64	best	good	ADJ
ejpam-1375	33	65	rbf	rbf	PROPN
ejpam-1375	33	66	.	.	PUNCT
ejpam-1375	34	1	then	then	ADV
ejpam-1375	34	2	,	,	PUNCT
ejpam-1375	34	3	we	we	PRON
ejpam-1375	34	4	carry	carry	VERB
ejpam-1375	34	5	out	out	ADP
ejpam-1375	34	6	a	a	DET
ejpam-1375	34	7	ga	ga	NOUN
ejpam-1375	34	8	subset	subset	NOUN
ejpam-1375	34	9	selection	selection	NOUN
ejpam-1375	34	10	of	of	ADP
ejpam-1375	34	11	best	good	ADJ
ejpam-1375	34	12	predictors	predictor	NOUN
ejpam-1375	34	13	and	and	CCONJ
ejpam-1375	34	14	give	give	VERB
ejpam-1375	34	15	the	the	DET
ejpam-1375	34	16	regression	regression	NOUN
ejpam-1375	34	17	tree	tree	NOUN
ejpam-1375	34	18	.	.	PUNCT
ejpam-1375	35	1	following	follow	VERB
ejpam-1375	35	2	this	this	PRON
ejpam-1375	35	3	,	,	PUNCT
ejpam-1375	35	4	we	we	PRON
ejpam-1375	35	5	construct	construct	VERB
ejpam-1375	35	6	the	the	DET
ejpam-1375	35	7	best	good	ADJ
ejpam-1375	35	8	predictive	predictive	ADJ
ejpam-1375	35	9	rbf	rbf	PROPN
ejpam-1375	35	10	-	-	PUNCT
ejpam-1375	35	11	nn	nn	PROPN
ejpam-1375	35	12	model	model	NOUN
ejpam-1375	35	13	based	base	VERB
ejpam-1375	35	14	on	on	ADP
ejpam-1375	35	15	the	the	DET
ejpam-1375	35	16	best	good	ADJ
ejpam-1375	35	17	predictors	predictor	NOUN
ejpam-1375	35	18	chosen	choose	VERB
ejpam-1375	35	19	.	.	PUNCT
ejpam-1375	36	1	as	as	ADP
ejpam-1375	36	2	an	an	DET
ejpam-1375	36	3	end	end	NOUN
ejpam-1375	36	4	result	result	NOUN
ejpam-1375	36	5	,	,	PUNCT
ejpam-1375	36	6	we	we	PRON
ejpam-1375	36	7	build	build	VERB
ejpam-1375	36	8	the	the	DET
ejpam-1375	36	9	final	final	ADJ
ejpam-1375	36	10	best	good	ADJ
ejpam-1375	36	11	fitting	fitting	ADJ
ejpam-1375	36	12	rbf	rbf	PROPN
ejpam-1375	36	13	-	-	PUNCT
ejpam-1375	36	14	nn	nn	PROPN
ejpam-1375	36	15	model	model	NOUN
ejpam-1375	36	16	in	in	ADP
ejpam-1375	36	17	its	its	PRON
ejpam-1375	36	18	open	open	ADJ
ejpam-1375	36	19	analytical	analytical	ADJ
ejpam-1375	36	20	form	form	NOUN
ejpam-1375	36	21	using	use	VERB
ejpam-1375	36	22	the	the	DET
ejpam-1375	36	23	recovered	recovered	ADJ
ejpam-1375	36	24	rbf	rbf	PROPN
ejpam-1375	36	25	centers	center	NOUN
ejpam-1375	36	26	,	,	PUNCT
ejpam-1375	36	27	c	c	X
ejpam-1375	36	28	,	,	PUNCT
ejpam-1375	36	29	radius	radius	NOUN
ejpam-1375	36	30	r	r	NOUN
ejpam-1375	36	31	,	,	PUNCT
ejpam-1375	36	32	and	and	CCONJ
ejpam-1375	36	33	the	the	DET
ejpam-1375	36	34	regression	regression	NOUN
ejpam-1375	36	35	weights	weight	NOUN
ejpam-1375	36	36	,	,	PUNCT
ejpam-1375	36	37	w.	w.	PROPN
ejpam-1375	36	38	although	although	SCONJ
ejpam-1375	36	39	the	the	DET
ejpam-1375	36	40	structure	structure	NOUN
ejpam-1375	36	41	of	of	ADP
ejpam-1375	36	42	hybrid	hybrid	ADJ
ejpam-1375	36	43	rbf	rbf	PROPN
ejpam-1375	36	44	-	-	PUNCT
ejpam-1375	36	45	nn	nn	PROPN
ejpam-1375	36	46	model	model	NOUN
ejpam-1375	36	47	represents	represent	VERB
ejpam-1375	36	48	a	a	DET
ejpam-1375	36	49	very	very	ADV
ejpam-1375	36	50	complicated	complicated	ADJ
ejpam-1375	36	51	equation	equation	NOUN
ejpam-1375	36	52	,	,	PUNCT
ejpam-1375	36	53	nevertheless	nevertheless	ADV
ejpam-1375	36	54	,	,	PUNCT
ejpam-1375	36	55	it	it	PRON
ejpam-1375	36	56	provides	provide	VERB
ejpam-1375	36	57	useful	useful	ADJ
ejpam-1375	36	58	information	information	NOUN
ejpam-1375	36	59	of	of	ADP
ejpam-1375	36	60	the	the	DET
ejpam-1375	36	61	structure	structure	NOUN
ejpam-1375	36	62	of	of	ADP
ejpam-1375	36	63	the	the	DET
ejpam-1375	36	64	predictive	predictive	ADJ
ejpam-1375	36	65	model	model	NOUN
ejpam-1375	36	66	which	which	PRON
ejpam-1375	36	67	is	be	AUX
ejpam-1375	36	68	nonlinear	nonlinear	ADJ
ejpam-1375	36	69	.	.	PUNCT
ejpam-1375	37	1	section	section	NOUN
ejpam-1375	37	2	9	9	NUM
ejpam-1375	37	3	concludes	conclude	VERB
ejpam-1375	37	4	the	the	DET
ejpam-1375	37	5	paper	paper	NOUN
ejpam-1375	37	6	.	.	PUNCT
ejpam-1375	38	1	2	2	X
ejpam-1375	38	2	.	.	PUNCT
ejpam-1375	38	3	linear	linear	ADJ
ejpam-1375	38	4	models	model	NOUN
ejpam-1375	38	5	and	and	CCONJ
ejpam-1375	38	6	radial	radial	ADJ
ejpam-1375	38	7	basis	basis	NOUN
ejpam-1375	38	8	function	function	NOUN
ejpam-1375	38	9	neural	neural	ADJ
ejpam-1375	38	10	networks	network	NOUN
ejpam-1375	38	11	(	(	PUNCT
ejpam-1375	38	12	rbf	rbf	PROPN
ejpam-1375	38	13	-	-	PUNCT
ejpam-1375	38	14	nn	nn	NOUN
ejpam-1375	38	15	)	)	PUNCT
ejpam-1375	38	16	2.1	2.1	NUM
ejpam-1375	38	17	.	.	PUNCT
ejpam-1375	39	1	linear	linear	NOUN
ejpam-1375	39	2	models	model	NOUN
ejpam-1375	39	3	we	we	PRON
ejpam-1375	39	4	shall	shall	AUX
ejpam-1375	39	5	consider	consider	VERB
ejpam-1375	39	6	supervised	supervised	ADJ
ejpam-1375	39	7	learning	learning	NOUN
ejpam-1375	39	8	,	,	PUNCT
ejpam-1375	39	9	or	or	CCONJ
ejpam-1375	39	10	what	what	PRON
ejpam-1375	39	11	statisticians	statistician	NOUN
ejpam-1375	39	12	call	call	VERB
ejpam-1375	39	13	,	,	PUNCT
ejpam-1375	39	14	nonparametric	nonparametric	NOUN
ejpam-1375	39	15	regression	regression	NOUN
ejpam-1375	39	16	problem	problem	NOUN
ejpam-1375	39	17	for	for	ADP
ejpam-1375	39	18	a	a	DET
ejpam-1375	39	19	given	give	VERB
ejpam-1375	39	20	multi	multi	ADJ
ejpam-1375	39	21	-	-	ADJ
ejpam-1375	39	22	dimensional	dimensional	ADJ
ejpam-1375	39	23	data	datum	NOUN
ejpam-1375	39	24	set	set	VERB
ejpam-1375	39	25	with	with	ADP
ejpam-1375	39	26	the	the	DET
ejpam-1375	39	27	dependent	dependent	ADJ
ejpam-1375	39	28	variable	variable	ADJ
ejpam-1375	39	29	y	y	PROPN
ejpam-1375	39	30	and	and	CCONJ
ejpam-1375	39	31	indepeno	indepeno	PROPN
ejpam-1375	39	32	.	.	PUNCT
ejpam-1375	40	1	akbilgic	akbilgic	PROPN
ejpam-1375	40	2	,	,	PUNCT
ejpam-1375	40	3	h.	h.	PROPN
ejpam-1375	40	4	bozdogan	bozdogan	PROPN
ejpam-1375	40	5	/	/	SYM
ejpam-1375	40	6	eur	eur	PROPN
ejpam-1375	40	7	.	.	PUNCT
ejpam-1375	41	1	j.	j.	PROPN
ejpam-1375	41	2	pure	pure	PROPN
ejpam-1375	41	3	appl	appl	PROPN
ejpam-1375	41	4	.	.	PROPN
ejpam-1375	41	5	math	math	PROPN
ejpam-1375	41	6	,	,	PUNCT
ejpam-1375	41	7	4	4	NUM
ejpam-1375	41	8	(	(	PUNCT
ejpam-1375	41	9	2011	2011	NUM
ejpam-1375	41	10	)	)	PUNCT
ejpam-1375	41	11	,	,	PUNCT
ejpam-1375	41	12	467	467	NUM
ejpam-1375	41	13	-	-	SYM
ejpam-1375	41	14	485	485	NUM
ejpam-1375	41	15	469	469	NUM
ejpam-1375	41	16	dent	dent	NOUN
ejpam-1375	41	17	(	(	PUNCT
ejpam-1375	41	18	or	or	CCONJ
ejpam-1375	41	19	predictor	predictor	NOUN
ejpam-1375	41	20	)	)	PUNCT
ejpam-1375	41	21	variables	variable	VERB
ejpam-1375	41	22	x1	x1	PROPN
ejpam-1375	41	23	,	,	PUNCT
ejpam-1375	41	24	x2	x2	PROPN
ejpam-1375	41	25	,	,	PUNCT
ejpam-1375	41	26	.	.	PUNCT
ejpam-1375	41	27	.	.	PUNCT
ejpam-1375	42	1	.	.	PUNCT
ejpam-1375	43	1	,	,	PUNCT
ejpam-1375	43	2	xm	xm	X
ejpam-1375	43	3	.	.	PUNCT
ejpam-1375	44	1	we	we	PRON
ejpam-1375	44	2	define	define	VERB
ejpam-1375	44	3	the	the	DET
ejpam-1375	44	4	general	general	ADJ
ejpam-1375	44	5	linear	linear	PROPN
ejpam-1375	44	6	model	model	NOUN
ejpam-1375	44	7	as	as	ADP
ejpam-1375	44	8	y	y	PROPN
ejpam-1375	44	9	=	=	SYM
ejpam-1375	44	10	f	f	PROPN
ejpam-1375	44	11	(	(	PUNCT
ejpam-1375	44	12	w	w	PROPN
ejpam-1375	44	13	,	,	PUNCT
ejpam-1375	44	14	x	x	NOUN
ejpam-1375	44	15	)	)	PUNCT
ejpam-1375	44	16	=	=	PUNCT
ejpam-1375	45	1	m∑	m∑	CCONJ
ejpam-1375	45	2	j=1	j=1	PROPN
ejpam-1375	45	3	w	w	PROPN
ejpam-1375	45	4	jh	jh	PROPN
ejpam-1375	45	5	j(x	j(x	PROPN
ejpam-1375	45	6	)	)	PUNCT
ejpam-1375	45	7	=	=	PUNCT
ejpam-1375	46	1	w1h1	w1h1	PUNCT
ejpam-1375	46	2	+	+	ADJ
ejpam-1375	46	3	w2h2	w2h2	X
ejpam-1375	46	4	+	+	PUNCT
ejpam-1375	46	5	.	.	PUNCT
ejpam-1375	46	6	.	.	PUNCT
ejpam-1375	47	1	.+wmhm	.+wmhm	INTJ
ejpam-1375	47	2	,	,	PUNCT
ejpam-1375	47	3	(	(	PUNCT
ejpam-1375	47	4	1	1	X
ejpam-1375	47	5	)	)	PUNCT
ejpam-1375	48	1	where	where	SCONJ
ejpam-1375	48	2	the	the	DET
ejpam-1375	48	3	regressors	regressor	NOUN
ejpam-1375	48	4	,	,	PUNCT
ejpam-1375	48	5	¦	¦	PROPN
ejpam-1375	48	6	h	h	PROPN
ejpam-1375	48	7	j(x	j(x	PROPN
ejpam-1375	48	8	)	)	PUNCT
ejpam-1375	49	1	©	©	PROPN
ejpam-1375	49	2	m	m	PROPN
ejpam-1375	49	3	j=1	j=1	NOUN
ejpam-1375	49	4	,	,	PUNCT
ejpam-1375	49	5	are	be	AUX
ejpam-1375	49	6	fixed	fix	VERB
ejpam-1375	49	7	basis	basis	NOUN
ejpam-1375	49	8	functions	function	NOUN
ejpam-1375	49	9	(	(	PUNCT
ejpam-1375	49	10	or	or	CCONJ
ejpam-1375	49	11	the	the	DET
ejpam-1375	49	12	transfer	transfer	NOUN
ejpam-1375	49	13	functions	function	NOUN
ejpam-1375	49	14	of	of	ADP
ejpam-1375	49	15	the	the	DET
ejpam-1375	49	16	hidden	hide	VERB
ejpam-1375	49	17	units	unit	NOUN
ejpam-1375	49	18	)	)	PUNCT
ejpam-1375	49	19	of	of	ADP
ejpam-1375	49	20	the	the	DET
ejpam-1375	49	21	predictors	predictor	NOUN
ejpam-1375	49	22	,	,	PUNCT
ejpam-1375	49	23	x	x	PUNCT
ejpam-1375	49	24	∈	∈	PROPN
ejpam-1375	49	25	ℜn	ℜn	PROPN
ejpam-1375	49	26	,	,	PUNCT
ejpam-1375	49	27	and	and	CCONJ
ejpam-1375	49	28	¦	¦	PROPN
ejpam-1375	49	29	w	w	PROPN
ejpam-1375	49	30	j	j	PROPN
ejpam-1375	50	1	©	©	PROPN
ejpam-1375	50	2	m	m	PROPN
ejpam-1375	50	3	j=1	j=1	NOUN
ejpam-1375	50	4	are	be	AUX
ejpam-1375	50	5	the	the	DET
ejpam-1375	50	6	unknown	unknown	ADJ
ejpam-1375	50	7	adaptable	adaptable	ADJ
ejpam-1375	50	8	coefficients	coefficient	NOUN
ejpam-1375	50	9	,	,	PUNCT
ejpam-1375	50	10	or	or	CCONJ
ejpam-1375	50	11	weights	weight	NOUN
ejpam-1375	50	12	.	.	PUNCT
ejpam-1375	51	1	to	to	PART
ejpam-1375	51	2	perform	perform	VERB
ejpam-1375	51	3	linear	linear	PROPN
ejpam-1375	51	4	regression	regression	NOUN
ejpam-1375	51	5	with	with	ADP
ejpam-1375	51	6	this	this	DET
ejpam-1375	51	7	model	model	NOUN
ejpam-1375	51	8	,	,	PUNCT
ejpam-1375	51	9	we	we	PRON
ejpam-1375	51	10	solve	solve	VERB
ejpam-1375	51	11	the	the	DET
ejpam-1375	51	12	following	follow	VERB
ejpam-1375	51	13	system	system	NOUN
ejpam-1375	51	14	of	of	ADP
ejpam-1375	51	15	equations	equation	NOUN
ejpam-1375	51	16	:	:	PUNCT
ejpam-1375	52	1	y	y	PROPN
ejpam-1375	52	2	=	=	PUNCT
ejpam-1375	52	3	hw	hw	PROPN
ejpam-1375	52	4	+	+	CCONJ
ejpam-1375	52	5	ǫ	ǫ	X
ejpam-1375	52	6	,	,	PUNCT
ejpam-1375	52	7	(	(	PUNCT
ejpam-1375	52	8	2	2	X
ejpam-1375	52	9	)	)	PUNCT
ejpam-1375	52	10	where	where	SCONJ
ejpam-1375	52	11	y	y	PROPN
ejpam-1375	52	12	is	be	AUX
ejpam-1375	52	13	a	a	DET
ejpam-1375	52	14	vector	vector	NOUN
ejpam-1375	52	15	of	of	ADP
ejpam-1375	52	16	(	(	PUNCT
ejpam-1375	52	17	n×1	n×1	PROPN
ejpam-1375	52	18	)	)	PUNCT
ejpam-1375	52	19	observations	observation	NOUN
ejpam-1375	52	20	on	on	ADP
ejpam-1375	52	21	a	a	DET
ejpam-1375	52	22	dependent	dependent	ADJ
ejpam-1375	52	23	variable	variable	NOUN
ejpam-1375	52	24	,	,	PUNCT
ejpam-1375	52	25	and	and	CCONJ
ejpam-1375	52	26	h	h	NOUN
ejpam-1375	52	27	is	be	AUX
ejpam-1375	52	28	a	a	DET
ejpam-1375	52	29	(	(	PUNCT
ejpam-1375	52	30	n×m	n×m	NOUN
ejpam-1375	52	31	)	)	PUNCT
ejpam-1375	52	32	design	design	NOUN
ejpam-1375	52	33	matrix	matrix	NOUN
ejpam-1375	52	34	and	and	CCONJ
ejpam-1375	52	35	are	be	AUX
ejpam-1375	52	36	responses	response	NOUN
ejpam-1375	52	37	of	of	ADP
ejpam-1375	52	38	m	m	NOUN
ejpam-1375	52	39	regressors	regressor	NOUN
ejpam-1375	52	40	given	give	VERB
ejpam-1375	52	41	by	by	ADP
ejpam-1375	52	42	h(n×m	h(n×m	PROPN
ejpam-1375	52	43	)	)	PUNCT
ejpam-1375	53	1	=	=	NOUN
ejpam-1375	53	2			NOUN
ejpam-1375	53	3			PROPN
ejpam-1375	53	4	h1(x1	h1(x1	PROPN
ejpam-1375	53	5	)	)	PUNCT
ejpam-1375	53	6	h2(x1	h2(x1	PROPN
ejpam-1375	53	7	)	)	PUNCT
ejpam-1375	53	8	·	·	PUNCT
ejpam-1375	53	9	·	·	PUNCT
ejpam-1375	53	10	·	·	PUNCT
ejpam-1375	53	11	hm(x1	hm(x1	X
ejpam-1375	53	12	)	)	PUNCT
ejpam-1375	53	13	h1(x2	h1(x2	NOUN
ejpam-1375	53	14	)	)	PUNCT
ejpam-1375	53	15	h2(x2	h2(x2	NOUN
ejpam-1375	53	16	)	)	PUNCT
ejpam-1375	53	17	·	·	PUNCT
ejpam-1375	53	18	·	·	PUNCT
ejpam-1375	53	19	·	·	PUNCT
ejpam-1375	54	1	hm(x2	hm(x2	X
ejpam-1375	54	2	)	)	PUNCT
ejpam-1375	54	3	...	...	PUNCT
ejpam-1375	54	4	...	...	PUNCT
ejpam-1375	54	5	.	.	PUNCT
ejpam-1375	54	6	.	.	PUNCT
ejpam-1375	54	7	.	.	PUNCT
ejpam-1375	54	8	...	...	PUNCT
ejpam-1375	55	1	h1(xn	h1(xn	X
ejpam-1375	55	2	)	)	PUNCT
ejpam-1375	55	3	h2(xn	h2(xn	NUM
ejpam-1375	55	4	)	)	PUNCT
ejpam-1375	55	5	h1(x1	h1(x1	NOUN
ejpam-1375	55	6	)	)	PUNCT
ejpam-1375	55	7	hm(xn	hm(xn	NOUN
ejpam-1375	55	8	)	)	PUNCT
ejpam-1375	55	9			PROPN
ejpam-1375	55	10			NOUN
ejpam-1375	55	11	.	.	PUNCT
ejpam-1375	56	1	(	(	PUNCT
ejpam-1375	56	2	3	3	X
ejpam-1375	56	3	)	)	PUNCT
ejpam-1375	56	4	in	in	ADP
ejpam-1375	56	5	(	(	PUNCT
ejpam-1375	56	6	2	2	NUM
ejpam-1375	56	7	)	)	PUNCT
ejpam-1375	56	8	,	,	PUNCT
ejpam-1375	56	9	w	w	PROPN
ejpam-1375	56	10	is	be	AUX
ejpam-1375	56	11	a	a	DET
ejpam-1375	56	12	(	(	PUNCT
ejpam-1375	56	13	n×	n×	NOUN
ejpam-1375	56	14	1	1	NUM
ejpam-1375	56	15	)	)	PUNCT
ejpam-1375	56	16	coefficient	coefficient	NOUN
ejpam-1375	56	17	vector	vector	NOUN
ejpam-1375	56	18	,	,	PUNCT
ejpam-1375	56	19	and	and	CCONJ
ejpam-1375	56	20	ǫ	ǫ	PRON
ejpam-1375	56	21	is	be	AUX
ejpam-1375	56	22	a	a	DET
ejpam-1375	56	23	(	(	PUNCT
ejpam-1375	56	24	n×	n×	NOUN
ejpam-1375	56	25	1	1	NUM
ejpam-1375	56	26	)	)	PUNCT
ejpam-1375	56	27	vector	vector	NOUN
ejpam-1375	56	28	of	of	ADP
ejpam-1375	56	29	random	random	ADJ
ejpam-1375	56	30	noise	noise	NOUN
ejpam-1375	56	31	term	term	NOUN
ejpam-1375	56	32	,	,	PUNCT
ejpam-1375	56	33	such	such	ADJ
ejpam-1375	56	34	that	that	SCONJ
ejpam-1375	56	35	ǫ	ǫ	PRON
ejpam-1375	56	36	∼	∼	NOUN
ejpam-1375	56	37	n(0,σ2i	n(0,σ2i	ADJ
ejpam-1375	56	38	)	)	PUNCT
ejpam-1375	56	39	or	or	CCONJ
ejpam-1375	56	40	equivalently	equivalently	ADV
ejpam-1375	56	41	ǫi	ǫi	ADP
ejpam-1375	56	42	∼	∼	NOUN
ejpam-1375	56	43	n(0,σ2i	n(0,σ2i	NUM
ejpam-1375	56	44	)	)	PUNCT
ejpam-1375	56	45	,	,	PUNCT
ejpam-1375	56	46	f	f	PROPN
ejpam-1375	56	47	or	or	CCONJ
ejpam-1375	56	48	i	i	NOUN
ejpam-1375	56	49	=	=	NOUN
ejpam-1375	56	50	1,2	1,2	NUM
ejpam-1375	56	51	,	,	PUNCT
ejpam-1375	56	52	.	.	PUNCT
ejpam-1375	56	53	.	.	PUNCT
ejpam-1375	57	1	.	.	PUNCT
ejpam-1375	58	1	,	,	PUNCT
ejpam-1375	58	2	n.	n.	PROPN
ejpam-1375	58	3	2.2	2.2	NUM
ejpam-1375	58	4	.	.	PUNCT
ejpam-1375	59	1	radial	radial	ADJ
ejpam-1375	59	2	basis	basis	NOUN
ejpam-1375	59	3	functions	function	NOUN
ejpam-1375	59	4	the	the	DET
ejpam-1375	59	5	flexibility	flexibility	NOUN
ejpam-1375	59	6	of	of	ADP
ejpam-1375	59	7	f	f	PROPN
ejpam-1375	59	8	in	in	ADP
ejpam-1375	59	9	(	(	PUNCT
ejpam-1375	59	10	1	1	NUM
ejpam-1375	59	11	)	)	PUNCT
ejpam-1375	59	12	stems	stem	VERB
ejpam-1375	59	13	from	from	ADP
ejpam-1375	59	14	the	the	DET
ejpam-1375	59	15	fact	fact	NOUN
ejpam-1375	59	16	that	that	SCONJ
ejpam-1375	59	17	we	we	PRON
ejpam-1375	59	18	can	can	AUX
ejpam-1375	59	19	consider	consider	VERB
ejpam-1375	59	20	and	and	CCONJ
ejpam-1375	59	21	fit	fit	VERB
ejpam-1375	59	22	many	many	ADJ
ejpam-1375	59	23	different	different	ADJ
ejpam-1375	59	24	radial	radial	ADJ
ejpam-1375	59	25	basis	basis	NOUN
ejpam-1375	59	26	functions	function	NOUN
ejpam-1375	59	27	(	(	PUNCT
ejpam-1375	59	28	rbfs	rbfs	NOUN
ejpam-1375	59	29	)	)	PUNCT
ejpam-1375	59	30	.	.	PUNCT
ejpam-1375	60	1	rbfs	rbfs	NOUN
ejpam-1375	60	2	are	be	AUX
ejpam-1375	60	3	one	one	NUM
ejpam-1375	60	4	possible	possible	ADJ
ejpam-1375	60	5	choice	choice	NOUN
ejpam-1375	60	6	for	for	ADP
ejpam-1375	60	7	the	the	DET
ejpam-1375	60	8	hidden	hide	VERB
ejpam-1375	60	9	unit	unit	NOUN
ejpam-1375	60	10	activation	activation	NOUN
ejpam-1375	60	11	functions	function	NOUN
ejpam-1375	60	12	in	in	ADP
ejpam-1375	60	13	a	a	DET
ejpam-1375	60	14	linear	linear	ADJ
ejpam-1375	60	15	network	network	NOUN
ejpam-1375	60	16	.	.	PUNCT
ejpam-1375	61	1	the	the	DET
ejpam-1375	61	2	most	most	ADV
ejpam-1375	61	3	distinguishing	distinguish	VERB
ejpam-1375	61	4	feature	feature	NOUN
ejpam-1375	61	5	of	of	ADP
ejpam-1375	61	6	these	these	DET
ejpam-1375	61	7	functions	function	NOUN
ejpam-1375	61	8	is	be	AUX
ejpam-1375	61	9	that	that	SCONJ
ejpam-1375	61	10	they	they	PRON
ejpam-1375	61	11	are	be	AUX
ejpam-1375	61	12	local	local	ADJ
ejpam-1375	61	13	,	,	PUNCT
ejpam-1375	61	14	or	or	CCONJ
ejpam-1375	61	15	at	at	ADP
ejpam-1375	61	16	least	least	ADJ
ejpam-1375	61	17	their	their	PRON
ejpam-1375	61	18	response	response	NOUN
ejpam-1375	61	19	decreases	decrease	VERB
ejpam-1375	61	20	monotonically	monotonically	ADV
ejpam-1375	61	21	away	away	ADV
ejpam-1375	61	22	from	from	ADP
ejpam-1375	61	23	a	a	DET
ejpam-1375	61	24	center	center	NOUN
ejpam-1375	61	25	point	point	NOUN
ejpam-1375	61	26	.	.	PUNCT
ejpam-1375	62	1	the	the	DET
ejpam-1375	62	2	rbfs	rbfs	NOUN
ejpam-1375	62	3	are	be	AUX
ejpam-1375	62	4	used	use	VERB
ejpam-1375	62	5	in	in	ADP
ejpam-1375	62	6	function	function	NOUN
ejpam-1375	62	7	approximation	approximation	NOUN
ejpam-1375	62	8	,	,	PUNCT
ejpam-1375	62	9	regularization	regularization	NOUN
ejpam-1375	62	10	,	,	PUNCT
ejpam-1375	62	11	noisy	noisy	ADJ
ejpam-1375	62	12	interpolation	interpolation	NOUN
ejpam-1375	62	13	,	,	PUNCT
ejpam-1375	62	14	density	density	NOUN
ejpam-1375	62	15	estimation	estimation	NOUN
ejpam-1375	62	16	optimal	optimal	ADJ
ejpam-1375	62	17	classification	classification	NOUN
ejpam-1375	62	18	and	and	CCONJ
ejpam-1375	62	19	clustering	clustering	NOUN
ejpam-1375	62	20	,	,	PUNCT
ejpam-1375	62	21	etc	etc	X
ejpam-1375	62	22	.	.	X
ejpam-1375	63	1	the	the	DET
ejpam-1375	63	2	rbfs	rbfs	NOUN
ejpam-1375	63	3	,	,	PUNCT
ejpam-1375	63	4	we	we	PRON
ejpam-1375	63	5	shall	shall	AUX
ejpam-1375	63	6	consider	consider	VERB
ejpam-1375	63	7	,	,	PUNCT
ejpam-1375	63	8	are	be	AUX
ejpam-1375	63	9	given	give	VERB
ejpam-1375	63	10	below	below	ADV
ejpam-1375	63	11	.	.	PUNCT
ejpam-1375	64	1	gaussian	gaussian	ADJ
ejpam-1375	64	2	kernel	kernel	PROPN
ejpam-1375	64	3	(	(	PUNCT
ejpam-1375	64	4	gk	gk	NOUN
ejpam-1375	64	5	):	):	PUNCT
ejpam-1375	64	6	h	h	PROPN
ejpam-1375	64	7	j(x	j(x	PROPN
ejpam-1375	64	8	)	)	PUNCT
ejpam-1375	64	9	=	=	PUNCT
ejpam-1375	65	1	ex	ex	PRON
ejpam-1375	65	2	p(−	p(−	NOUN
ejpam-1375	65	3	p∑	p∑	X
ejpam-1375	66	1	k=1	k=1	X
ejpam-1375	67	1	(	(	PUNCT
ejpam-1375	67	2	xk−	xk−	PROPN
ejpam-1375	67	3	c	c	PROPN
ejpam-1375	67	4	jk	jk	PROPN
ejpam-1375	67	5	)	)	PUNCT
ejpam-1375	67	6	2	2	NUM
ejpam-1375	67	7	r2	r2	PROPN
ejpam-1375	67	8	jk	jk	PROPN
ejpam-1375	67	9	)	)	PUNCT
ejpam-1375	67	10	(	(	PUNCT
ejpam-1375	67	11	4	4	X
ejpam-1375	67	12	)	)	PUNCT
ejpam-1375	67	13	cauchy	cauchy	ADJ
ejpam-1375	67	14	kernel	kernel	NOUN
ejpam-1375	67	15	(	(	PUNCT
ejpam-1375	67	16	ck	ck	NOUN
ejpam-1375	67	17	):	):	PUNCT
ejpam-1375	67	18	h	h	PROPN
ejpam-1375	67	19	j(x	j(x	PROPN
ejpam-1375	67	20	)	)	PUNCT
ejpam-1375	67	21	=	=	SYM
ejpam-1375	67	22	1	1	NUM
ejpam-1375	67	23	1	1	NUM
ejpam-1375	67	24	+	+	NUM
ejpam-1375	67	25	ex	ex	PRON
ejpam-1375	67	26	p(−∑p	p(−∑p	NOUN
ejpam-1375	67	27	k=1	k=1	X
ejpam-1375	67	28	(	(	PUNCT
ejpam-1375	67	29	xk−c	xk−c	PROPN
ejpam-1375	67	30	jk	jk	PROPN
ejpam-1375	67	31	)	)	PUNCT
ejpam-1375	67	32	2	2	NUM
ejpam-1375	67	33	r2	r2	PROPN
ejpam-1375	67	34	jk	jk	PROPN
ejpam-1375	67	35	)	)	PUNCT
ejpam-1375	67	36	(	(	PUNCT
ejpam-1375	67	37	5	5	X
ejpam-1375	67	38	)	)	PUNCT
ejpam-1375	67	39	multiquadric	multiquadric	ADJ
ejpam-1375	67	40	kernel	kernel	PROPN
ejpam-1375	67	41	(	(	PUNCT
ejpam-1375	67	42	mlqk	mlqk	PROPN
ejpam-1375	67	43	):	):	PUNCT
ejpam-1375	67	44	h	h	PROPN
ejpam-1375	67	45	j(x	j(x	PROPN
ejpam-1375	67	46	)	)	PUNCT
ejpam-1375	68	1	=	=	PUNCT
ejpam-1375	68	2	s	s	PART
ejpam-1375	68	3	1	1	NUM
ejpam-1375	68	4	+	+	NUM
ejpam-1375	68	5	ex	ex	PRON
ejpam-1375	68	6	p(−σp	p(−σp	NOUN
ejpam-1375	68	7	k=1	k=1	PROPN
ejpam-1375	69	1	(	(	PUNCT
ejpam-1375	69	2	xk	xk	INTJ
ejpam-1375	69	3	−	−	PROPN
ejpam-1375	69	4	c	c	PROPN
ejpam-1375	69	5	jk	jk	PROPN
ejpam-1375	69	6	)	)	PUNCT
ejpam-1375	69	7	2	2	NUM
ejpam-1375	69	8	r2	r2	PROPN
ejpam-1375	69	9	jk	jk	PROPN
ejpam-1375	69	10	)	)	PUNCT
ejpam-1375	69	11	(	(	PUNCT
ejpam-1375	69	12	6	6	X
ejpam-1375	69	13	)	)	PUNCT
ejpam-1375	69	14	o.	o.	NOUN
ejpam-1375	69	15	akbilgic	akbilgic	PROPN
ejpam-1375	69	16	,	,	PUNCT
ejpam-1375	69	17	h.	h.	PROPN
ejpam-1375	69	18	bozdogan	bozdogan	PROPN
ejpam-1375	69	19	/	/	SYM
ejpam-1375	69	20	eur	eur	PROPN
ejpam-1375	69	21	.	.	PUNCT
ejpam-1375	70	1	j.	j.	PROPN
ejpam-1375	70	2	pure	pure	PROPN
ejpam-1375	70	3	appl	appl	PROPN
ejpam-1375	70	4	.	.	PROPN
ejpam-1375	70	5	math	math	PROPN
ejpam-1375	70	6	,	,	PUNCT
ejpam-1375	70	7	4	4	NUM
ejpam-1375	70	8	(	(	PUNCT
ejpam-1375	70	9	2011	2011	NUM
ejpam-1375	70	10	)	)	PUNCT
ejpam-1375	70	11	,	,	PUNCT
ejpam-1375	70	12	467	467	NUM
ejpam-1375	70	13	-	-	SYM
ejpam-1375	70	14	485	485	NUM
ejpam-1375	70	15	470	470	NUM
ejpam-1375	70	16	inverse	inverse	NOUN
ejpam-1375	70	17	multiquadric	multiquadric	ADJ
ejpam-1375	70	18	kernel	kernel	PROPN
ejpam-1375	70	19	(	(	PUNCT
ejpam-1375	70	20	imlqk	imlqk	NOUN
ejpam-1375	70	21	):	):	PUNCT
ejpam-1375	70	22	h	h	PROPN
ejpam-1375	70	23	j(x	j(x	PROPN
ejpam-1375	70	24	)	)	PUNCT
ejpam-1375	71	1	=	=	PUNCT
ejpam-1375	71	2	1ç	1ç	NOUN
ejpam-1375	71	3	1	1	NUM
ejpam-1375	71	4	+	+	NUM
ejpam-1375	71	5	ex	ex	PRON
ejpam-1375	71	6	p(−σp	p(−σp	NOUN
ejpam-1375	71	7	k=1	k=1	X
ejpam-1375	72	1	(	(	PUNCT
ejpam-1375	72	2	xk−c	xk−c	PROPN
ejpam-1375	72	3	jk	jk	PROPN
ejpam-1375	72	4	)	)	PUNCT
ejpam-1375	72	5	2	2	NUM
ejpam-1375	72	6	r2	r2	PROPN
ejpam-1375	72	7	jk	jk	PROPN
ejpam-1375	72	8	)	)	PUNCT
ejpam-1375	72	9	(	(	PUNCT
ejpam-1375	72	10	7	7	X
ejpam-1375	72	11	)	)	PUNCT
ejpam-1375	72	12	2.3	2.3	NUM
ejpam-1375	72	13	.	.	PUNCT
ejpam-1375	73	1	radial	radial	ADJ
ejpam-1375	73	2	basis	basis	NOUN
ejpam-1375	73	3	function	function	NOUN
ejpam-1375	73	4	neural	neural	ADJ
ejpam-1375	73	5	networks	network	NOUN
ejpam-1375	73	6	the	the	DET
ejpam-1375	73	7	rbf	rbf	PROPN
ejpam-1375	73	8	-	-	PUNCT
ejpam-1375	73	9	nn	nn	PROPN
ejpam-1375	73	10	introduces	introduce	NOUN
ejpam-1375	73	11	a	a	DET
ejpam-1375	73	12	mapping	mapping	NOUN
ejpam-1375	73	13	or	or	CCONJ
ejpam-1375	73	14	transformation	transformation	NOUN
ejpam-1375	73	15	of	of	ADP
ejpam-1375	73	16	the	the	DET
ejpam-1375	73	17	n	n	ADV
ejpam-1375	73	18	-	-	PUNCT
ejpam-1375	73	19	dimensional	dimensional	ADJ
ejpam-1375	73	20	inputs	input	NOUN
ejpam-1375	73	21	nonlinearly	nonlinearly	ADV
ejpam-1375	73	22	to	to	ADP
ejpam-1375	73	23	an	an	DET
ejpam-1375	73	24	m	m	ADV
ejpam-1375	73	25	-	-	ADJ
ejpam-1375	73	26	dimensional	dimensional	ADJ
ejpam-1375	73	27	space	space	NOUN
ejpam-1375	73	28	and	and	CCONJ
ejpam-1375	73	29	then	then	ADV
ejpam-1375	73	30	estimate	estimate	VERB
ejpam-1375	73	31	a	a	DET
ejpam-1375	73	32	model	model	NOUN
ejpam-1375	73	33	using	use	VERB
ejpam-1375	73	34	linear	linear	PROPN
ejpam-1375	73	35	regression	regression	NOUN
ejpam-1375	73	36	.	.	PUNCT
ejpam-1375	74	1	the	the	DET
ejpam-1375	74	2	nonlinear	nonlinear	ADJ
ejpam-1375	74	3	transformation	transformation	NOUN
ejpam-1375	74	4	is	be	AUX
ejpam-1375	74	5	achieved	achieve	VERB
ejpam-1375	74	6	using	use	VERB
ejpam-1375	74	7	m	m	PROPN
ejpam-1375	74	8	basis	basis	NOUN
ejpam-1375	74	9	functions	function	NOUN
ejpam-1375	74	10	,	,	PUNCT
ejpam-1375	74	11	each	each	PRON
ejpam-1375	74	12	characterized	characterize	VERB
ejpam-1375	74	13	by	by	ADP
ejpam-1375	74	14	their	their	PRON
ejpam-1375	74	15	center	center	NOUN
ejpam-1375	74	16	c	c	PROPN
ejpam-1375	74	17	j	j	PROPN
ejpam-1375	74	18	in	in	ADP
ejpam-1375	74	19	the	the	DET
ejpam-1375	74	20	(	(	PUNCT
ejpam-1375	74	21	original	original	ADJ
ejpam-1375	74	22	)	)	PUNCT
ejpam-1375	74	23	input	input	NOUN
ejpam-1375	74	24	space	space	NOUN
ejpam-1375	74	25	and	and	CCONJ
ejpam-1375	74	26	a	a	DET
ejpam-1375	74	27	width	width	ADJ
ejpam-1375	74	28	or	or	CCONJ
ejpam-1375	74	29	radius	radius	NOUN
ejpam-1375	74	30	vector	vector	NOUN
ejpam-1375	74	31	r	r	PROPN
ejpam-1375	74	32	j	j	PROPN
ejpam-1375	74	33	,	,	PUNCT
ejpam-1375	74	34	j	j	PROPN
ejpam-1375	74	35	∈	∈	PROPN
ejpam-1375	74	36	{	{	PUNCT
ejpam-1375	74	37	1,2	1,2	NUM
ejpam-1375	74	38	,	,	PUNCT
ejpam-1375	74	39	.	.	PUNCT
ejpam-1375	74	40	.	.	PUNCT
ejpam-1375	75	1	.	.	PUNCT
ejpam-1375	76	1	,	,	PUNCT
ejpam-1375	76	2	m	m	VERB
ejpam-1375	76	3	}	}	PUNCT
ejpam-1375	77	1	[	[	X
ejpam-1375	77	2	19	19	NUM
ejpam-1375	77	3	]	]	PUNCT
ejpam-1375	77	4	.	.	PUNCT
ejpam-1375	78	1	in	in	ADP
ejpam-1375	78	2	principle	principle	NOUN
ejpam-1375	78	3	,	,	PUNCT
ejpam-1375	78	4	rbfs	rbfs	NOUN
ejpam-1375	78	5	can	can	AUX
ejpam-1375	78	6	be	be	AUX
ejpam-1375	78	7	used	use	VERB
ejpam-1375	78	8	in	in	ADP
ejpam-1375	78	9	any	any	DET
ejpam-1375	78	10	sort	sort	NOUN
ejpam-1375	78	11	of	of	ADP
ejpam-1375	78	12	modeling	modeling	NOUN
ejpam-1375	78	13	,	,	PUNCT
ejpam-1375	78	14	whether	whether	SCONJ
ejpam-1375	78	15	they	they	PRON
ejpam-1375	78	16	are	be	AUX
ejpam-1375	78	17	linear	linear	ADJ
ejpam-1375	78	18	or	or	CCONJ
ejpam-1375	78	19	nonlinear	nonlinear	ADJ
ejpam-1375	78	20	and	and	CCONJ
ejpam-1375	78	21	for	for	ADP
ejpam-1375	78	22	single	single	ADJ
ejpam-1375	78	23	-	-	PUNCT
ejpam-1375	78	24	layer	layer	NOUN
ejpam-1375	78	25	or	or	CCONJ
ejpam-1375	78	26	multi	multi	ADJ
ejpam-1375	78	27	-	-	ADJ
ejpam-1375	78	28	layer	layer	ADJ
ejpam-1375	78	29	networks	network	NOUN
ejpam-1375	78	30	.	.	PUNCT
ejpam-1375	79	1	[	[	X
ejpam-1375	79	2	20	20	NUM
ejpam-1375	79	3	]	]	PUNCT
ejpam-1375	79	4	has	have	AUX
ejpam-1375	79	5	shown	show	VERB
ejpam-1375	79	6	that	that	SCONJ
ejpam-1375	79	7	rbf	rbf	PROPN
ejpam-1375	79	8	-	-	PUNCT
ejpam-1375	79	9	nn	nn	PROPN
ejpam-1375	79	10	possess	possess	VERB
ejpam-1375	79	11	the	the	DET
ejpam-1375	79	12	property	property	NOUN
ejpam-1375	79	13	of	of	ADP
ejpam-1375	79	14	best	good	ADJ
ejpam-1375	79	15	approximation	approximation	NOUN
ejpam-1375	79	16	.	.	PUNCT
ejpam-1375	80	1	2.4	2.4	NUM
ejpam-1375	80	2	.	.	PUNCT
ejpam-1375	81	1	least	least	ADJ
ejpam-1375	81	2	squares	square	NOUN
ejpam-1375	81	3	estimation	estimation	NOUN
ejpam-1375	81	4	given	give	VERB
ejpam-1375	81	5	a	a	DET
ejpam-1375	81	6	network	network	NOUN
ejpam-1375	81	7	(	(	PUNCT
ejpam-1375	81	8	or	or	CCONJ
ejpam-1375	81	9	model	model	NOUN
ejpam-1375	81	10	)	)	PUNCT
ejpam-1375	81	11	in	in	ADP
ejpam-1375	81	12	(	(	PUNCT
ejpam-1375	81	13	1	1	X
ejpam-1375	81	14	)	)	PUNCT
ejpam-1375	81	15	consisting	consist	VERB
ejpam-1375	81	16	of	of	ADP
ejpam-1375	81	17	m	m	PROPN
ejpam-1375	81	18	rbfs	rbfs	NOUN
ejpam-1375	81	19	with	with	ADP
ejpam-1375	81	20	centers	center	NOUN
ejpam-1375	82	1	¦	¦	PROPN
ejpam-1375	82	2	c	c	PROPN
ejpam-1375	82	3	j	j	PROPN
ejpam-1375	83	1	©	©	PROPN
ejpam-1375	83	2	m	m	PROPN
ejpam-1375	83	3	j=1	j=1	NOUN
ejpam-1375	83	4	and	and	CCONJ
ejpam-1375	83	5	radii	radii	VERB
ejpam-1375	83	6	¦	¦	NOUN
ejpam-1375	83	7	r	r	NOUN
ejpam-1375	83	8	j	j	PROPN
ejpam-1375	84	1	©	©	PROPN
ejpam-1375	84	2	m	m	PROPN
ejpam-1375	84	3	j=1	j=1	NOUN
ejpam-1375	84	4	and	and	CCONJ
ejpam-1375	84	5	a	a	DET
ejpam-1375	84	6	training	training	NOUN
ejpam-1375	84	7	set	set	VERB
ejpam-1375	84	8	with	with	ADP
ejpam-1375	84	9	p	p	NOUN
ejpam-1375	84	10	patterns	pattern	NOUN
ejpam-1375	84	11	,	,	PUNCT
ejpam-1375	84	12	�	�	PROPN
ejpam-1375	84	13	�	�	PROPN
ejpam-1375	84	14	x	x	SYM
ejpam-1375	84	15	i	i	PROPN
ejpam-1375	84	16	,	,	PUNCT
ejpam-1375	84	17	yi	yi	PROPN
ejpam-1375	84	18	�	�	PROPN
ejpam-1375	85	1	p	p	X
ejpam-1375	85	2	i=1	i=1	PROPN
ejpam-1375	85	3	,	,	PUNCT
ejpam-1375	85	4	the	the	DET
ejpam-1375	85	5	optimal	optimal	ADJ
ejpam-1375	85	6	network	network	NOUN
ejpam-1375	85	7	weights	weight	NOUN
ejpam-1375	85	8	can	can	AUX
ejpam-1375	85	9	be	be	AUX
ejpam-1375	85	10	found	find	VERB
ejpam-1375	85	11	by	by	ADP
ejpam-1375	85	12	minimizing	minimize	VERB
ejpam-1375	85	13	the	the	DET
ejpam-1375	85	14	sum	sum	NOUN
ejpam-1375	85	15	of	of	ADP
ejpam-1375	85	16	squared	square	VERB
ejpam-1375	85	17	errors	error	NOUN
ejpam-1375	85	18	:	:	PUNCT
ejpam-1375	86	1	sse	sse	X
ejpam-1375	86	2	=	=	PUNCT
ejpam-1375	86	3	p∑	p∑	X
ejpam-1375	86	4	i=1	i=1	PROPN
ejpam-1375	86	5	�	�	PROPN
ejpam-1375	87	1	f	f	PROPN
ejpam-1375	87	2	(	(	PUNCT
ejpam-1375	87	3	x	x	PROPN
ejpam-1375	87	4	i)−	i)−	PROPN
ejpam-1375	87	5	yi	yi	NOUN
ejpam-1375	87	6	�	�	PROPN
ejpam-1375	87	7	2	2	NUM
ejpam-1375	87	8	(	(	PUNCT
ejpam-1375	87	9	8)	8)	NUM
ejpam-1375	87	10	and	and	CCONJ
ejpam-1375	87	11	is	be	AUX
ejpam-1375	87	12	given	give	VERB
ejpam-1375	87	13	by	by	ADP
ejpam-1375	87	14	ŵ	ŵ	X
ejpam-1375	87	15	=	=	SYM
ejpam-1375	87	16	�	�	PROPN
ejpam-1375	87	17	h	h	NOUN
ejpam-1375	87	18	′	′	NUM
ejpam-1375	87	19	h	h	PROPN
ejpam-1375	87	20	�	�	PROPN
ejpam-1375	87	21	−1	−1	NOUN
ejpam-1375	87	22	h	h	NOUN
ejpam-1375	87	23	′	′	NUM
ejpam-1375	88	1	y	y	NOUN
ejpam-1375	88	2	(	(	PUNCT
ejpam-1375	88	3	9	9	NUM
ejpam-1375	88	4	)	)	PUNCT
ejpam-1375	88	5	the	the	DET
ejpam-1375	88	6	so	so	ADV
ejpam-1375	88	7	called	call	VERB
ejpam-1375	88	8	normal	normal	ADJ
ejpam-1375	88	9	equation	equation	NOUN
ejpam-1375	88	10	.	.	PUNCT
ejpam-1375	89	1	here	here	ADV
ejpam-1375	89	2	h	h	NOUN
ejpam-1375	89	3	is	be	AUX
ejpam-1375	89	4	the	the	DET
ejpam-1375	89	5	design	design	NOUN
ejpam-1375	89	6	matrix	matrix	NOUN
ejpam-1375	89	7	,	,	PUNCT
ejpam-1375	89	8	with	with	ADP
ejpam-1375	89	9	its	its	PRON
ejpam-1375	89	10	elements	element	NOUN
ejpam-1375	89	11	hi	hi	INTJ
ejpam-1375	90	1	j	j	PROPN
ejpam-1375	91	1	=	=	SYM
ejpam-1375	92	1	h	h	PROPN
ejpam-1375	93	1	j(x	j(x	PROPN
ejpam-1375	94	1	i	i	PROPN
ejpam-1375	94	2	)	)	PUNCT
ejpam-1375	94	3	,	,	PUNCT
ejpam-1375	94	4	and	and	CCONJ
ejpam-1375	94	5	y	y	PROPN
ejpam-1375	94	6	=	=	SYM
ejpam-1375	94	7	�	�	PROPN
ejpam-1375	94	8	y1	y1	PROPN
ejpam-1375	94	9	,	,	PUNCT
ejpam-1375	94	10	y2	y2	PROPN
ejpam-1375	94	11	,	,	PUNCT
ejpam-1375	94	12	.	.	PUNCT
ejpam-1375	94	13	.	.	PUNCT
ejpam-1375	94	14	.	.	PUNCT
ejpam-1375	95	1	,	,	PUNCT
ejpam-1375	95	2	yp	yp	PROPN
ejpam-1375	95	3	�	�	PROPN
ejpam-1375	95	4	′	′	PROPN
ejpam-1375	95	5	is	be	AUX
ejpam-1375	95	6	the	the	DET
ejpam-1375	95	7	p	p	ADJ
ejpam-1375	95	8	-	-	PUNCT
ejpam-1375	95	9	dimensional	dimensional	ADJ
ejpam-1375	95	10	vector	vector	NOUN
ejpam-1375	95	11	of	of	ADP
ejpam-1375	95	12	training	training	NOUN
ejpam-1375	95	13	set	set	VERB
ejpam-1375	95	14	output	output	NOUN
ejpam-1375	95	15	values	value	NOUN
ejpam-1375	95	16	.	.	PUNCT
ejpam-1375	96	1	3	3	X
ejpam-1375	96	2	.	.	X
ejpam-1375	96	3	combining	combine	VERB
ejpam-1375	96	4	regression	regression	NOUN
ejpam-1375	96	5	trees	tree	NOUN
ejpam-1375	96	6	and	and	CCONJ
ejpam-1375	96	7	rbfnn	rbfnn	VERB
ejpam-1375	96	8	3.1	3.1	NUM
ejpam-1375	96	9	.	.	PUNCT
ejpam-1375	97	1	regression	regression	NOUN
ejpam-1375	97	2	trees	tree	NOUN
ejpam-1375	97	3	the	the	DET
ejpam-1375	97	4	basic	basic	ADJ
ejpam-1375	97	5	idea	idea	NOUN
ejpam-1375	97	6	of	of	ADP
ejpam-1375	97	7	rt	rt	PROPN
ejpam-1375	97	8	is	be	AUX
ejpam-1375	97	9	to	to	PART
ejpam-1375	97	10	partition	partition	VERB
ejpam-1375	97	11	the	the	DET
ejpam-1375	97	12	input	input	NOUN
ejpam-1375	97	13	space	space	NOUN
ejpam-1375	97	14	recursively	recursively	ADV
ejpam-1375	97	15	into	into	ADP
ejpam-1375	97	16	two	two	NUM
ejpam-1375	97	17	,	,	PUNCT
ejpam-1375	97	18	and	and	CCONJ
ejpam-1375	97	19	approximate	approximate	VERB
ejpam-1375	97	20	the	the	DET
ejpam-1375	97	21	function	function	NOUN
ejpam-1375	97	22	in	in	ADP
ejpam-1375	97	23	each	each	DET
ejpam-1375	97	24	half	half	NOUN
ejpam-1375	97	25	by	by	ADP
ejpam-1375	97	26	the	the	DET
ejpam-1375	97	27	average	average	ADJ
ejpam-1375	97	28	output	output	NOUN
ejpam-1375	97	29	value	value	NOUN
ejpam-1375	97	30	of	of	ADP
ejpam-1375	97	31	the	the	DET
ejpam-1375	97	32	samples	sample	NOUN
ejpam-1375	97	33	it	it	PRON
ejpam-1375	97	34	contains	contain	VERB
ejpam-1375	97	35	to	to	PART
ejpam-1375	97	36	refine	refine	VERB
ejpam-1375	97	37	the	the	DET
ejpam-1375	97	38	subset	subset	ADJ
ejpam-1375	97	39	variable	variable	ADJ
ejpam-1375	97	40	selection	selection	NOUN
ejpam-1375	97	41	[	[	X
ejpam-1375	97	42	9	9	NUM
ejpam-1375	97	43	]	]	PUNCT
ejpam-1375	97	44	.	.	PUNCT
ejpam-1375	98	1	each	each	DET
ejpam-1375	98	2	split	split	NOUN
ejpam-1375	98	3	is	be	AUX
ejpam-1375	98	4	parallel	parallel	ADJ
ejpam-1375	98	5	to	to	ADP
ejpam-1375	98	6	one	one	NUM
ejpam-1375	98	7	of	of	ADP
ejpam-1375	98	8	the	the	DET
ejpam-1375	98	9	axes	axis	NOUN
ejpam-1375	98	10	and	and	CCONJ
ejpam-1375	98	11	can	can	AUX
ejpam-1375	98	12	be	be	AUX
ejpam-1375	98	13	expressed	express	VERB
ejpam-1375	98	14	as	as	ADP
ejpam-1375	98	15	an	an	DET
ejpam-1375	98	16	inequality	inequality	NOUN
ejpam-1375	98	17	involving	involve	VERB
ejpam-1375	98	18	of	of	ADP
ejpam-1375	98	19	the	the	DET
ejpam-1375	98	20	input	input	NOUN
ejpam-1375	98	21	components	component	NOUN
ejpam-1375	98	22	�	�	PROPN
ejpam-1375	98	23	e.g.xk	e.g.xk	PROPN
ejpam-1375	98	24	>	>	X
ejpam-1375	98	25	b	b	PROPN
ejpam-1375	98	26	�	�	PROPN
ejpam-1375	98	27	.	.	PUNCT
ejpam-1375	99	1	the	the	DET
ejpam-1375	99	2	input	input	NOUN
ejpam-1375	99	3	space	space	NOUN
ejpam-1375	99	4	is	be	AUX
ejpam-1375	99	5	divided	divide	VERB
ejpam-1375	99	6	into	into	ADP
ejpam-1375	99	7	hyperrectangles	hyperrectangle	NOUN
ejpam-1375	99	8	organized	organize	VERB
ejpam-1375	99	9	into	into	ADP
ejpam-1375	99	10	a	a	DET
ejpam-1375	99	11	binary	binary	ADJ
ejpam-1375	99	12	tree	tree	NOUN
ejpam-1375	99	13	where	where	SCONJ
ejpam-1375	99	14	each	each	DET
ejpam-1375	99	15	branch	branch	NOUN
ejpam-1375	99	16	is	be	AUX
ejpam-1375	99	17	determined	determine	VERB
ejpam-1375	99	18	by	by	ADP
ejpam-1375	99	19	the	the	DET
ejpam-1375	99	20	dimension	dimension	NOUN
ejpam-1375	99	21	(	(	PUNCT
ejpam-1375	99	22	k	k	NOUN
ejpam-1375	99	23	)	)	PUNCT
ejpam-1375	99	24	and	and	CCONJ
ejpam-1375	99	25	boundary	boundary	ADJ
ejpam-1375	99	26	(	(	PUNCT
ejpam-1375	99	27	b	b	NOUN
ejpam-1375	99	28	)	)	PUNCT
ejpam-1375	99	29	which	which	PRON
ejpam-1375	99	30	together	together	ADV
ejpam-1375	99	31	minimize	minimize	VERB
ejpam-1375	99	32	the	the	DET
ejpam-1375	99	33	residual	residual	ADJ
ejpam-1375	99	34	error	error	NOUN
ejpam-1375	99	35	between	between	ADP
ejpam-1375	99	36	model	model	NOUN
ejpam-1375	99	37	and	and	CCONJ
ejpam-1375	99	38	data	datum	NOUN
ejpam-1375	99	39	[	[	X
ejpam-1375	99	40	19	19	NUM
ejpam-1375	99	41	]	]	PUNCT
ejpam-1375	99	42	.	.	PUNCT
ejpam-1375	100	1	the	the	DET
ejpam-1375	100	2	root	root	NOUN
ejpam-1375	100	3	node	node	NOUN
ejpam-1375	100	4	of	of	ADP
ejpam-1375	100	5	the	the	DET
ejpam-1375	100	6	regression	regression	NOUN
ejpam-1375	100	7	tree	tree	NOUN
ejpam-1375	100	8	is	be	AUX
ejpam-1375	100	9	the	the	DET
ejpam-1375	100	10	smallest	small	ADJ
ejpam-1375	100	11	hyperrectangle	hyperrectangle	NOUN
ejpam-1375	100	12	that	that	PRON
ejpam-1375	100	13	will	will	AUX
ejpam-1375	100	14	include	include	VERB
ejpam-1375	100	15	all	all	PRON
ejpam-1375	100	16	of	of	ADP
ejpam-1375	100	17	the	the	DET
ejpam-1375	100	18	training	training	NOUN
ejpam-1375	100	19	data	datum	NOUN
ejpam-1375	100	20	�	�	PROPN
ejpam-1375	100	21	x	x	PUNCT
ejpam-1375	101	1	i	i	PRON
ejpam-1375	101	2	p	p	X
ejpam-1375	101	3	i=1	i=1	X
ejpam-1375	101	4	.	.	PUNCT
ejpam-1375	102	1	its	its	PRON
ejpam-1375	102	2	size	size	NOUN
ejpam-1375	102	3	sk	sk	ADP
ejpam-1375	102	4	(	(	PUNCT
ejpam-1375	102	5	half	half	ADJ
ejpam-1375	102	6	–	–	PUNCT
ejpam-1375	102	7	width	width	ADJ
ejpam-1375	102	8	)	)	PUNCT
ejpam-1375	102	9	and	and	CCONJ
ejpam-1375	102	10	center	center	NOUN
ejpam-1375	102	11	ck	ck	INTJ
ejpam-1375	102	12	in	in	ADP
ejpam-1375	102	13	each	each	DET
ejpam-1375	102	14	dimension	dimension	NOUN
ejpam-1375	102	15	o.	o.	PROPN
ejpam-1375	102	16	akbilgic	akbilgic	PROPN
ejpam-1375	102	17	,	,	PUNCT
ejpam-1375	102	18	h.	h.	PROPN
ejpam-1375	102	19	bozdogan	bozdogan	PROPN
ejpam-1375	102	20	/	/	SYM
ejpam-1375	102	21	eur	eur	PROPN
ejpam-1375	102	22	.	.	PUNCT
ejpam-1375	103	1	j.	j.	PROPN
ejpam-1375	103	2	pure	pure	PROPN
ejpam-1375	103	3	appl	appl	PROPN
ejpam-1375	103	4	.	.	PROPN
ejpam-1375	103	5	math	math	PROPN
ejpam-1375	103	6	,	,	PUNCT
ejpam-1375	103	7	4	4	NUM
ejpam-1375	103	8	(	(	PUNCT
ejpam-1375	103	9	2011	2011	NUM
ejpam-1375	103	10	)	)	PUNCT
ejpam-1375	103	11	,	,	PUNCT
ejpam-1375	103	12	467	467	NUM
ejpam-1375	103	13	-	-	SYM
ejpam-1375	103	14	485	485	NUM
ejpam-1375	103	15	471	471	NUM
ejpam-1375	103	16	k	k	NOUN
ejpam-1375	103	17	are	be	AUX
ejpam-1375	103	18	sk	sk	INTJ
ejpam-1375	103	19	=	=	SYM
ejpam-1375	103	20	1	1	NUM
ejpam-1375	103	21	2	2	NUM
ejpam-1375	103	22	�	�	PROPN
ejpam-1375	103	23	max	max	PROPN
ejpam-1375	103	24	i∈s	i∈s	ADJ
ejpam-1375	103	25	�	�	PROPN
ejpam-1375	103	26	x	x	PROPN
ejpam-1375	103	27	ik	ik	PROPN
ejpam-1375	103	28	�	�	PROPN
ejpam-1375	103	29	−min	−min	NOUN
ejpam-1375	103	30	i∈s	i∈s	ADJ
ejpam-1375	103	31	�	�	PROPN
ejpam-1375	103	32	x	x	SYM
ejpam-1375	103	33	ik	ik	PROPN
ejpam-1375	103	34	�	�	PROPN
ejpam-1375	103	35	�	�	PROPN
ejpam-1375	103	36	(	(	PUNCT
ejpam-1375	103	37	10	10	NUM
ejpam-1375	103	38	)	)	PUNCT
ejpam-1375	103	39	ck	ck	NOUN
ejpam-1375	104	1	=	=	SYM
ejpam-1375	104	2	1	1	NUM
ejpam-1375	104	3	2	2	NUM
ejpam-1375	104	4	�	�	PROPN
ejpam-1375	104	5	max	max	PROPN
ejpam-1375	104	6	i∈s	i∈s	ADJ
ejpam-1375	104	7	�	�	PROPN
ejpam-1375	104	8	x	x	SYM
ejpam-1375	104	9	ik	ik	PROPN
ejpam-1375	104	10	�	�	PROPN
ejpam-1375	104	11	+	+	NOUN
ejpam-1375	104	12	min	min	NOUN
ejpam-1375	104	13	i∈s	i∈s	ADJ
ejpam-1375	104	14	�	�	PROPN
ejpam-1375	104	15	x	x	PROPN
ejpam-1375	104	16	ik	ik	PROPN
ejpam-1375	104	17	�	�	PROPN
ejpam-1375	104	18	�	�	PROPN
ejpam-1375	104	19	(	(	PUNCT
ejpam-1375	104	20	11	11	NUM
ejpam-1375	104	21	)	)	PUNCT
ejpam-1375	104	22	where	where	SCONJ
ejpam-1375	104	23	k	k	PROPN
ejpam-1375	104	24	∈	∈	PROPN
ejpam-1375	104	25	k	k	PROPN
ejpam-1375	104	26	is	be	AUX
ejpam-1375	104	27	the	the	DET
ejpam-1375	104	28	set	set	NOUN
ejpam-1375	104	29	of	of	ADP
ejpam-1375	104	30	predictor	predictor	NOUN
ejpam-1375	104	31	indices	index	NOUN
ejpam-1375	104	32	,	,	PUNCT
ejpam-1375	104	33	and	and	CCONJ
ejpam-1375	104	34	s	s	NOUN
ejpam-1375	104	35	=	=	PUNCT
ejpam-1375	104	36	�	�	PROPN
ejpam-1375	104	37	1,2	1,2	NUM
ejpam-1375	104	38	,	,	PUNCT
ejpam-1375	104	39	.	.	PUNCT
ejpam-1375	104	40	.	.	PUNCT
ejpam-1375	105	1	.	.	PUNCT
ejpam-1375	106	1	,	,	PUNCT
ejpam-1375	106	2	p	p	NOUN
ejpam-1375	106	3	is	be	AUX
ejpam-1375	106	4	the	the	DET
ejpam-1375	106	5	set	set	NOUN
ejpam-1375	106	6	of	of	ADP
ejpam-1375	106	7	training	training	NOUN
ejpam-1375	106	8	set	set	NOUN
ejpam-1375	106	9	indices	index	NOUN
ejpam-1375	106	10	.	.	PUNCT
ejpam-1375	107	1	a	a	DET
ejpam-1375	107	2	split	split	NOUN
ejpam-1375	107	3	of	of	ADP
ejpam-1375	107	4	the	the	DET
ejpam-1375	107	5	root	root	NOUN
ejpam-1375	107	6	node	node	NOUN
ejpam-1375	107	7	divides	divide	VERB
ejpam-1375	107	8	the	the	DET
ejpam-1375	107	9	training	training	NOUN
ejpam-1375	107	10	samples	sample	NOUN
ejpam-1375	107	11	into	into	ADP
ejpam-1375	107	12	left	left	ADJ
ejpam-1375	107	13	and	and	CCONJ
ejpam-1375	107	14	right	right	ADJ
ejpam-1375	107	15	subsets	subset	NOUN
ejpam-1375	107	16	,	,	PUNCT
ejpam-1375	107	17	sl	sl	PROPN
ejpam-1375	107	18	and	and	CCONJ
ejpam-1375	107	19	sr	sr	PROPN
ejpam-1375	107	20	,	,	PUNCT
ejpam-1375	107	21	on	on	ADP
ejpam-1375	107	22	either	either	DET
ejpam-1375	107	23	side	side	NOUN
ejpam-1375	107	24	of	of	ADP
ejpam-1375	107	25	a	a	DET
ejpam-1375	107	26	boundary	boundary	ADJ
ejpam-1375	107	27	b	b	NOUN
ejpam-1375	107	28	in	in	ADP
ejpam-1375	107	29	one	one	NUM
ejpam-1375	107	30	of	of	ADP
ejpam-1375	107	31	the	the	DET
ejpam-1375	107	32	dimensions	dimension	NOUN
ejpam-1375	107	33	k	k	X
ejpam-1375	107	34	such	such	ADJ
ejpam-1375	107	35	that	that	DET
ejpam-1375	107	36	sl	sl	PROPN
ejpam-1375	107	37	=	=	SYM
ejpam-1375	107	38	�	�	PROPN
ejpam-1375	108	1	i	i	PRON
ejpam-1375	108	2	:	:	PUNCT
ejpam-1375	108	3	x	x	PROPN
ejpam-1375	108	4	ik	ik	PROPN
ejpam-1375	108	5	≤	≤	PROPN
ejpam-1375	108	6	b	b	PROPN
ejpam-1375	108	7	,	,	PUNCT
ejpam-1375	108	8	(	(	PUNCT
ejpam-1375	108	9	12	12	NUM
ejpam-1375	108	10	)	)	PUNCT
ejpam-1375	108	11	sr	sr	PROPN
ejpam-1375	108	12	=	=	SYM
ejpam-1375	108	13	�	�	PROPN
ejpam-1375	109	1	i	i	PRON
ejpam-1375	109	2	:	:	PUNCT
ejpam-1375	109	3	x	x	SYM
ejpam-1375	109	4	ik	ik	PROPN
ejpam-1375	109	5	>	>	X
ejpam-1375	109	6	b	b	PROPN
ejpam-1375	109	7	.	.	PUNCT
ejpam-1375	110	1	(	(	PUNCT
ejpam-1375	110	2	13	13	NUM
ejpam-1375	110	3	)	)	PUNCT
ejpam-1375	110	4	the	the	DET
ejpam-1375	110	5	mean	mean	ADJ
ejpam-1375	110	6	output	output	NOUN
ejpam-1375	110	7	value	value	NOUN
ejpam-1375	110	8	on	on	ADP
ejpam-1375	110	9	either	either	DET
ejpam-1375	110	10	side	side	NOUN
ejpam-1375	110	11	of	of	ADP
ejpam-1375	110	12	the	the	DET
ejpam-1375	110	13	bifurcation	bifurcation	NOUN
ejpam-1375	110	14	is	be	AUX
ejpam-1375	110	15	y	y	PROPN
ejpam-1375	110	16	l	l	NOUN
ejpam-1375	110	17	=	=	SYM
ejpam-1375	110	18	1	1	NUM
ejpam-1375	110	19	pl	pl	NOUN
ejpam-1375	110	20	∑	∑	PUNCT
ejpam-1375	110	21	i∈sl	i∈sl	PROPN
ejpam-1375	110	22	yi	yi	PROPN
ejpam-1375	110	23	,	,	PUNCT
ejpam-1375	110	24	(	(	PUNCT
ejpam-1375	110	25	14	14	NUM
ejpam-1375	110	26	)	)	PUNCT
ejpam-1375	110	27	yr	yr	NOUN
ejpam-1375	110	28	=	=	SYM
ejpam-1375	110	29	1	1	NUM
ejpam-1375	110	30	pr	pr	NOUN
ejpam-1375	110	31	∑	∑	PROPN
ejpam-1375	110	32	i∈sr	i∈sr	PROPN
ejpam-1375	110	33	yi	yi	PROPN
ejpam-1375	110	34	,	,	PUNCT
ejpam-1375	110	35	(	(	PUNCT
ejpam-1375	110	36	15	15	NUM
ejpam-1375	110	37	)	)	PUNCT
ejpam-1375	110	38	where	where	SCONJ
ejpam-1375	110	39	pl	pl	NOUN
ejpam-1375	110	40	and	and	CCONJ
ejpam-1375	110	41	pr	pr	NOUN
ejpam-1375	110	42	are	be	AUX
ejpam-1375	110	43	the	the	DET
ejpam-1375	110	44	number	number	NOUN
ejpam-1375	110	45	of	of	ADP
ejpam-1375	110	46	samples	sample	NOUN
ejpam-1375	110	47	in	in	ADP
ejpam-1375	110	48	each	each	DET
ejpam-1375	110	49	subset	subset	NOUN
ejpam-1375	110	50	.	.	PUNCT
ejpam-1375	111	1	the	the	DET
ejpam-1375	111	2	mean	mean	ADJ
ejpam-1375	111	3	square	square	ADJ
ejpam-1375	111	4	error	error	NOUN
ejpam-1375	111	5	(	(	PUNCT
ejpam-1375	111	6	mse	mse	NOUN
ejpam-1375	111	7	)	)	PUNCT
ejpam-1375	111	8	is	be	AUX
ejpam-1375	111	9	then	then	ADV
ejpam-1375	111	10	calculated	calculate	VERB
ejpam-1375	111	11	as	as	ADP
ejpam-1375	111	12	in	in	ADP
ejpam-1375	111	13	equation	equation	NOUN
ejpam-1375	111	14	(	(	PUNCT
ejpam-1375	111	15	16	16	NUM
ejpam-1375	111	16	)	)	PUNCT
ejpam-1375	111	17	.	.	PUNCT
ejpam-1375	112	1	mse(k	mse(k	NOUN
ejpam-1375	112	2	,	,	PUNCT
ejpam-1375	112	3	b	b	NOUN
ejpam-1375	112	4	)	)	PUNCT
ejpam-1375	112	5	=	=	SYM
ejpam-1375	112	6	1	1	NUM
ejpam-1375	112	7	p	p	NOUN
ejpam-1375	112	8			NOUN
ejpam-1375	112	9			ADJ
ejpam-1375	112	10	∑	∑	ADV
ejpam-1375	112	11	i∈sl	i∈sl	PROPN
ejpam-1375	112	12	�	�	PROPN
ejpam-1375	112	13	yi	yi	PROPN
ejpam-1375	112	14	−	−	PROPN
ejpam-1375	112	15	y	y	PROPN
ejpam-1375	112	16	l	l	NOUN
ejpam-1375	112	17	�	�	PROPN
ejpam-1375	112	18	2	2	NUM
ejpam-1375	112	19	+	+	NOUN
ejpam-1375	112	20	∑	∑	PROPN
ejpam-1375	112	21	i∈sr	i∈sr	PROPN
ejpam-1375	112	22	�	�	PROPN
ejpam-1375	112	23	yi	yi	PROPN
ejpam-1375	112	24	−	−	PROPN
ejpam-1375	112	25	yr	yr	PROPN
ejpam-1375	112	26	�	�	PROPN
ejpam-1375	112	27	2	2	NUM
ejpam-1375	112	28			X
ejpam-1375	112	29			NOUN
ejpam-1375	112	30	(	(	PUNCT
ejpam-1375	112	31	16	16	NUM
ejpam-1375	112	32	)	)	PUNCT
ejpam-1375	112	33	the	the	DET
ejpam-1375	112	34	split	split	NOUN
ejpam-1375	112	35	which	which	PRON
ejpam-1375	112	36	minimizes	minimize	VERB
ejpam-1375	112	37	mse	mse	NOUN
ejpam-1375	112	38	(	(	PUNCT
ejpam-1375	112	39	k	k	NOUN
ejpam-1375	112	40	,	,	PUNCT
ejpam-1375	112	41	b	b	NOUN
ejpam-1375	112	42	)	)	PUNCT
ejpam-1375	112	43	over	over	ADP
ejpam-1375	112	44	all	all	DET
ejpam-1375	112	45	possible	possible	ADJ
ejpam-1375	112	46	choices	choice	NOUN
ejpam-1375	112	47	of	of	ADP
ejpam-1375	112	48	k	k	PROPN
ejpam-1375	112	49	and	and	CCONJ
ejpam-1375	112	50	b	b	PROPN
ejpam-1375	112	51	is	be	AUX
ejpam-1375	112	52	used	use	VERB
ejpam-1375	112	53	to	to	PART
ejpam-1375	112	54	create	create	VERB
ejpam-1375	112	55	the	the	DET
ejpam-1375	112	56	“	"	PUNCT
ejpam-1375	112	57	children	child	NOUN
ejpam-1375	112	58	”	"	PUNCT
ejpam-1375	112	59	of	of	ADP
ejpam-1375	112	60	the	the	DET
ejpam-1375	112	61	root	root	NOUN
ejpam-1375	112	62	node	node	NOUN
ejpam-1375	112	63	and	and	CCONJ
ejpam-1375	112	64	is	be	AUX
ejpam-1375	112	65	found	find	VERB
ejpam-1375	112	66	by	by	ADP
ejpam-1375	112	67	simple	simple	ADJ
ejpam-1375	112	68	discrete	discrete	ADJ
ejpam-1375	112	69	search	search	NOUN
ejpam-1375	112	70	over	over	ADP
ejpam-1375	112	71	m	m	NOUN
ejpam-1375	112	72	dimensions	dimension	NOUN
ejpam-1375	112	73	and	and	CCONJ
ejpam-1375	112	74	p	p	NOUN
ejpam-1375	112	75	observations	observation	NOUN
ejpam-1375	112	76	.	.	PUNCT
ejpam-1375	113	1	the	the	DET
ejpam-1375	113	2	children	child	NOUN
ejpam-1375	113	3	of	of	ADP
ejpam-1375	113	4	the	the	DET
ejpam-1375	113	5	root	root	NOUN
ejpam-1375	113	6	node	node	NOUN
ejpam-1375	113	7	are	be	AUX
ejpam-1375	113	8	split	split	VERB
ejpam-1375	113	9	recursively	recursively	ADV
ejpam-1375	113	10	in	in	ADP
ejpam-1375	113	11	the	the	DET
ejpam-1375	113	12	same	same	ADJ
ejpam-1375	113	13	manner	manner	NOUN
ejpam-1375	113	14	and	and	CCONJ
ejpam-1375	113	15	the	the	DET
ejpam-1375	113	16	process	process	NOUN
ejpam-1375	113	17	terminates	terminate	VERB
ejpam-1375	113	18	when	when	SCONJ
ejpam-1375	113	19	every	every	DET
ejpam-1375	113	20	remaining	remain	VERB
ejpam-1375	113	21	split	split	NOUN
ejpam-1375	113	22	creates	create	VERB
ejpam-1375	113	23	children	child	NOUN
ejpam-1375	113	24	containing	contain	VERB
ejpam-1375	113	25	fewer	few	ADJ
ejpam-1375	113	26	than	than	ADP
ejpam-1375	113	27	pmin	pmin	NOUN
ejpam-1375	113	28	samples	sample	NOUN
ejpam-1375	113	29	,	,	PUNCT
ejpam-1375	113	30	which	which	PRON
ejpam-1375	113	31	is	be	AUX
ejpam-1375	113	32	a	a	DET
ejpam-1375	113	33	parameter	parameter	NOUN
ejpam-1375	113	34	of	of	ADP
ejpam-1375	113	35	the	the	DET
ejpam-1375	113	36	method	method	NOUN
ejpam-1375	113	37	.	.	PUNCT
ejpam-1375	114	1	the	the	DET
ejpam-1375	114	2	children	child	NOUN
ejpam-1375	114	3	are	be	AUX
ejpam-1375	114	4	shifted	shift	VERB
ejpam-1375	114	5	with	with	ADP
ejpam-1375	114	6	respect	respect	NOUN
ejpam-1375	114	7	to	to	ADP
ejpam-1375	114	8	their	their	PRON
ejpam-1375	114	9	parent	parent	NOUN
ejpam-1375	114	10	nodes	node	NOUN
ejpam-1375	114	11	and	and	CCONJ
ejpam-1375	114	12	their	their	PRON
ejpam-1375	114	13	sizes	size	NOUN
ejpam-1375	114	14	reduced	reduce	VERB
ejpam-1375	114	15	in	in	ADP
ejpam-1375	114	16	the	the	DET
ejpam-1375	114	17	k−	k−	PROPN
ejpam-1375	114	18	th	th	NUM
ejpam-1375	114	19	dimension	dimension	NOUN
ejpam-1375	114	20	.	.	PUNCT
ejpam-1375	115	1	rt	rt	PROPN
ejpam-1375	115	2	can	can	AUX
ejpam-1375	115	3	both	both	PRON
ejpam-1375	115	4	estimate	estimate	VERB
ejpam-1375	115	5	a	a	DET
ejpam-1375	115	6	model	model	NOUN
ejpam-1375	115	7	and	and	CCONJ
ejpam-1375	115	8	indicate	indicate	VERB
ejpam-1375	115	9	which	which	DET
ejpam-1375	115	10	components	component	NOUN
ejpam-1375	115	11	of	of	ADP
ejpam-1375	115	12	the	the	DET
ejpam-1375	115	13	input	input	NOUN
ejpam-1375	115	14	vector	vector	NOUN
ejpam-1375	115	15	most	most	ADV
ejpam-1375	115	16	relevant	relevant	ADJ
ejpam-1375	115	17	to	to	ADP
ejpam-1375	115	18	the	the	DET
ejpam-1375	115	19	modeled	model	VERB
ejpam-1375	115	20	relationship	relationship	NOUN
ejpam-1375	115	21	.	.	PUNCT
ejpam-1375	116	1	dimensions	dimension	NOUN
ejpam-1375	116	2	which	which	PRON
ejpam-1375	116	3	carry	carry	VERB
ejpam-1375	116	4	the	the	DET
ejpam-1375	116	5	most	most	ADJ
ejpam-1375	116	6	information	information	NOUN
ejpam-1375	116	7	about	about	ADP
ejpam-1375	116	8	the	the	DET
ejpam-1375	116	9	output	output	NOUN
ejpam-1375	116	10	tend	tend	VERB
ejpam-1375	116	11	to	to	PART
ejpam-1375	116	12	split	split	VERB
ejpam-1375	116	13	earliest	early	ADJ
ejpam-1375	116	14	and	and	CCONJ
ejpam-1375	116	15	most	most	ADV
ejpam-1375	116	16	often	often	ADV
ejpam-1375	116	17	[	[	X
ejpam-1375	116	18	19	19	NUM
ejpam-1375	116	19	]	]	PUNCT
ejpam-1375	116	20	.	.	PUNCT
ejpam-1375	117	1	3.2	3.2	NUM
ejpam-1375	117	2	.	.	PUNCT
ejpam-1375	118	1	transforming	transform	VERB
ejpam-1375	118	2	tree	tree	NOUN
ejpam-1375	118	3	nodes	node	NOUN
ejpam-1375	118	4	into	into	ADP
ejpam-1375	118	5	rbfs	rbfs	NOUN
ejpam-1375	118	6	the	the	DET
ejpam-1375	118	7	regression	regression	NOUN
ejpam-1375	118	8	tree	tree	NOUN
ejpam-1375	118	9	contains	contain	VERB
ejpam-1375	118	10	a	a	DET
ejpam-1375	118	11	root	root	NOUN
ejpam-1375	118	12	node	node	NOUN
ejpam-1375	118	13	,	,	PUNCT
ejpam-1375	118	14	some	some	DET
ejpam-1375	118	15	nonterminal	nonterminal	ADJ
ejpam-1375	118	16	nodes	node	NOUN
ejpam-1375	118	17	(	(	PUNCT
ejpam-1375	118	18	having	have	VERB
ejpam-1375	118	19	children	child	NOUN
ejpam-1375	118	20	)	)	PUNCT
ejpam-1375	118	21	and	and	CCONJ
ejpam-1375	118	22	some	some	DET
ejpam-1375	118	23	terminal	terminal	ADJ
ejpam-1375	118	24	nodes	node	NOUN
ejpam-1375	118	25	(	(	PUNCT
ejpam-1375	118	26	having	have	VERB
ejpam-1375	118	27	no	no	DET
ejpam-1375	118	28	children	child	NOUN
ejpam-1375	118	29	)	)	PUNCT
ejpam-1375	118	30	.	.	PUNCT
ejpam-1375	119	1	each	each	DET
ejpam-1375	119	2	node	node	NOUN
ejpam-1375	119	3	is	be	AUX
ejpam-1375	119	4	associated	associate	VERB
ejpam-1375	119	5	with	with	ADP
ejpam-1375	119	6	a	a	DET
ejpam-1375	119	7	hyperrectangle	hyperrectangle	NOUN
ejpam-1375	119	8	of	of	ADP
ejpam-1375	119	9	input	input	NOUN
ejpam-1375	119	10	space	space	NOUN
ejpam-1375	119	11	having	have	VERB
ejpam-1375	119	12	a	a	DET
ejpam-1375	119	13	center	center	NOUN
ejpam-1375	119	14	c	c	NOUN
ejpam-1375	119	15	and	and	CCONJ
ejpam-1375	119	16	size	size	NOUN
ejpam-1375	119	17	s	s	PRON
ejpam-1375	119	18	as	as	SCONJ
ejpam-1375	119	19	described	describe	VERB
ejpam-1375	119	20	above	above	ADV
ejpam-1375	119	21	.	.	PUNCT
ejpam-1375	120	1	the	the	DET
ejpam-1375	120	2	node	node	NOUN
ejpam-1375	120	3	corresponding	correspond	VERB
ejpam-1375	120	4	to	to	ADP
ejpam-1375	120	5	the	the	DET
ejpam-1375	120	6	largest	large	ADJ
ejpam-1375	120	7	hyperrectangle	hyperrectangle	NOUN
ejpam-1375	120	8	is	be	AUX
ejpam-1375	120	9	the	the	DET
ejpam-1375	120	10	root	root	NOUN
ejpam-1375	120	11	node	node	NOUN
ejpam-1375	120	12	and	and	CCONJ
ejpam-1375	120	13	that	that	PRON
ejpam-1375	120	14	is	be	AUX
ejpam-1375	120	15	divided	divide	VERB
ejpam-1375	120	16	up	up	ADP
ejpam-1375	120	17	into	into	ADP
ejpam-1375	120	18	smaller	small	ADJ
ejpam-1375	120	19	and	and	CCONJ
ejpam-1375	120	20	smaller	small	ADJ
ejpam-1375	120	21	pieces	piece	NOUN
ejpam-1375	120	22	progressing	progress	VERB
ejpam-1375	120	23	down	down	ADP
ejpam-1375	120	24	the	the	DET
ejpam-1375	120	25	tree	tree	NOUN
ejpam-1375	120	26	.	.	PUNCT
ejpam-1375	121	1	to	to	PART
ejpam-1375	121	2	transform	transform	VERB
ejpam-1375	121	3	the	the	DET
ejpam-1375	121	4	hyperrectangle	hyperrectangle	NOUN
ejpam-1375	121	5	into	into	ADP
ejpam-1375	121	6	different	different	ADJ
ejpam-1375	121	7	basis	basis	NOUN
ejpam-1375	121	8	kernel	kernel	NOUN
ejpam-1375	121	9	rbfs	rbfs	PROPN
ejpam-1375	121	10	o.	o.	PROPN
ejpam-1375	121	11	akbilgic	akbilgic	PROPN
ejpam-1375	121	12	,	,	PUNCT
ejpam-1375	121	13	h.	h.	PROPN
ejpam-1375	121	14	bozdogan	bozdogan	PROPN
ejpam-1375	121	15	/	/	SYM
ejpam-1375	121	16	eur	eur	PROPN
ejpam-1375	121	17	.	.	PUNCT
ejpam-1375	122	1	j.	j.	PROPN
ejpam-1375	122	2	pure	pure	PROPN
ejpam-1375	122	3	appl	appl	PROPN
ejpam-1375	122	4	.	.	PROPN
ejpam-1375	122	5	math	math	PROPN
ejpam-1375	122	6	,	,	PUNCT
ejpam-1375	122	7	4	4	NUM
ejpam-1375	122	8	(	(	PUNCT
ejpam-1375	122	9	2011	2011	NUM
ejpam-1375	122	10	)	)	PUNCT
ejpam-1375	122	11	,	,	PUNCT
ejpam-1375	122	12	467	467	NUM
ejpam-1375	122	13	-	-	SYM
ejpam-1375	122	14	485	485	NUM
ejpam-1375	122	15	472	472	NUM
ejpam-1375	122	16	we	we	PRON
ejpam-1375	122	17	use	use	VERB
ejpam-1375	122	18	its	its	PRON
ejpam-1375	122	19	center	center	NOUN
ejpam-1375	122	20	c	c	NOUN
ejpam-1375	122	21	as	as	ADP
ejpam-1375	122	22	the	the	DET
ejpam-1375	122	23	rbf	rbf	PROPN
ejpam-1375	122	24	center	center	NOUN
ejpam-1375	122	25	and	and	CCONJ
ejpam-1375	122	26	its	its	PRON
ejpam-1375	122	27	size	size	NOUN
ejpam-1375	122	28	s	s	PROPN
ejpam-1375	122	29	,	,	PUNCT
ejpam-1375	122	30	scaled	scale	VERB
ejpam-1375	122	31	by	by	ADP
ejpam-1375	122	32	a	a	DET
ejpam-1375	122	33	parameter	parameter	NOUN
ejpam-1375	122	34	α	α	NOUN
ejpam-1375	122	35	as	as	ADP
ejpam-1375	122	36	the	the	DET
ejpam-1375	122	37	rbf	rbf	PROPN
ejpam-1375	122	38	radius	radius	NOUN
ejpam-1375	122	39	given	give	VERB
ejpam-1375	122	40	by	by	ADP
ejpam-1375	122	41	r	r	NOUN
ejpam-1375	122	42	=	=	SYM
ejpam-1375	122	43	αs	αs	PROPN
ejpam-1375	122	44	.	.	PUNCT
ejpam-1375	123	1	(	(	PUNCT
ejpam-1375	123	2	17	17	NUM
ejpam-1375	123	3	)	)	PUNCT
ejpam-1375	123	4	the	the	DET
ejpam-1375	123	5	scalar	scalar	ADJ
ejpam-1375	123	6	α	α	PROPN
ejpam-1375	123	7	has	have	VERB
ejpam-1375	123	8	the	the	DET
ejpam-1375	123	9	same	same	ADJ
ejpam-1375	123	10	value	value	NOUN
ejpam-1375	123	11	for	for	ADP
ejpam-1375	123	12	all	all	DET
ejpam-1375	123	13	nodes	node	NOUN
ejpam-1375	123	14	(	(	PUNCT
ejpam-1375	123	15	kubat	kubat	NOUN
ejpam-1375	123	16	,	,	PUNCT
ejpam-1375	123	17	1998	1998	NUM
ejpam-1375	123	18	)	)	PUNCT
ejpam-1375	123	19	,	,	PUNCT
ejpam-1375	123	20	and	and	CCONJ
ejpam-1375	123	21	it	it	PRON
ejpam-1375	123	22	is	be	AUX
ejpam-1375	123	23	another	another	DET
ejpam-1375	123	24	parameter	parameter	NOUN
ejpam-1375	123	25	of	of	ADP
ejpam-1375	123	26	the	the	DET
ejpam-1375	123	27	method	method	NOUN
ejpam-1375	123	28	.	.	PUNCT
ejpam-1375	124	1	one	one	PRON
ejpam-1375	124	2	can	can	AUX
ejpam-1375	124	3	use	use	VERB
ejpam-1375	124	4	α=	α=	NOUN
ejpam-1375	124	5	p	p	NOUN
ejpam-1375	124	6	2α−1	2α−1	NUM
ejpam-1375	124	7	k	k	X
ejpam-1375	124	8	where	where	SCONJ
ejpam-1375	124	9	αk	αk	PRON
ejpam-1375	124	10	is	be	AUX
ejpam-1375	124	11	the	the	DET
ejpam-1375	124	12	kubat	kubat	NOUN
ejpam-1375	124	13	’s	’s	PART
ejpam-1375	124	14	parameter	parameter	NOUN
ejpam-1375	125	1	[	[	X
ejpam-1375	125	2	12	12	NUM
ejpam-1375	125	3	,	,	PUNCT
ejpam-1375	125	4	19	19	NUM
ejpam-1375	125	5	]	]	PUNCT
ejpam-1375	125	6	.	.	PUNCT
ejpam-1375	126	1	4	4	X
ejpam-1375	126	2	.	.	X
ejpam-1375	126	3	subset	subset	ADJ
ejpam-1375	126	4	selection	selection	NOUN
ejpam-1375	126	5	of	of	ADP
ejpam-1375	126	6	rbfs	rbfs	NOUN
ejpam-1375	126	7	and	and	CCONJ
ejpam-1375	126	8	current	current	ADJ
ejpam-1375	126	9	problems	problem	NOUN
ejpam-1375	126	10	after	after	SCONJ
ejpam-1375	126	11	the	the	DET
ejpam-1375	126	12	tree	tree	NOUN
ejpam-1375	126	13	nodes	node	NOUN
ejpam-1375	126	14	are	be	AUX
ejpam-1375	126	15	transformed	transform	VERB
ejpam-1375	126	16	into	into	ADP
ejpam-1375	126	17	rbfs	rbfs	NOUN
ejpam-1375	126	18	,	,	PUNCT
ejpam-1375	126	19	the	the	DET
ejpam-1375	126	20	next	next	ADJ
ejpam-1375	126	21	step	step	NOUN
ejpam-1375	126	22	of	of	ADP
ejpam-1375	126	23	the	the	DET
ejpam-1375	126	24	method	method	NOUN
ejpam-1375	126	25	is	be	AUX
ejpam-1375	126	26	to	to	PART
ejpam-1375	126	27	carry	carry	VERB
ejpam-1375	126	28	out	out	ADP
ejpam-1375	126	29	a	a	DET
ejpam-1375	126	30	subset	subset	ADJ
ejpam-1375	126	31	selection	selection	NOUN
ejpam-1375	126	32	of	of	ADP
ejpam-1375	126	33	variables	variable	NOUN
ejpam-1375	126	34	to	to	PART
ejpam-1375	126	35	be	be	AUX
ejpam-1375	126	36	included	include	VERB
ejpam-1375	126	37	in	in	ADP
ejpam-1375	126	38	the	the	DET
ejpam-1375	126	39	model	model	NOUN
ejpam-1375	126	40	to	to	PART
ejpam-1375	126	41	choose	choose	VERB
ejpam-1375	126	42	the	the	DET
ejpam-1375	126	43	best	well	ADV
ejpam-1375	126	44	fitting	fitting	ADJ
ejpam-1375	126	45	subset(s	subset(s	NOUN
ejpam-1375	126	46	)	)	PUNCT
ejpam-1375	126	47	.	.	PUNCT
ejpam-1375	127	1	current	current	ADJ
ejpam-1375	127	2	standard	standard	ADJ
ejpam-1375	127	3	techniques	technique	NOUN
ejpam-1375	127	4	for	for	ADP
ejpam-1375	127	5	variable	variable	ADJ
ejpam-1375	127	6	selection	selection	NOUN
ejpam-1375	127	7	include	include	VERB
ejpam-1375	127	8	:	:	PUNCT
ejpam-1375	127	9	•	•	NUM
ejpam-1375	127	10	forward	forward	ADJ
ejpam-1375	127	11	selection	selection	NOUN
ejpam-1375	127	12	:	:	PUNCT
ejpam-1375	127	13	the	the	DET
ejpam-1375	127	14	basis	basis	NOUN
ejpam-1375	127	15	kernel	kernel	PROPN
ejpam-1375	127	16	rbfs	rbfs	NOUN
ejpam-1375	127	17	are	be	AUX
ejpam-1375	127	18	added	add	VERB
ejpam-1375	127	19	until	until	SCONJ
ejpam-1375	127	20	-	-	PUNCT
ejpam-1375	127	21	over	over	ADP
ejpam-1375	127	22	-	-	PUNCT
ejpam-1375	127	23	fitting	fitting	NOUN
ejpam-1375	127	24	occurs	occur	VERB
ejpam-1375	127	25	•	•	NUM
ejpam-1375	127	26	backward	backward	ADJ
ejpam-1375	127	27	elimination	elimination	NOUN
ejpam-1375	127	28	:	:	PUNCT
ejpam-1375	127	29	the	the	DET
ejpam-1375	127	30	basis	basis	NOUN
ejpam-1375	127	31	kernel	kernel	PROPN
ejpam-1375	127	32	rbfs	rbfs	NOUN
ejpam-1375	127	33	are	be	AUX
ejpam-1375	127	34	pruned	prune	VERB
ejpam-1375	127	35	until	until	SCONJ
ejpam-1375	127	36	over	over	ADP
ejpam-1375	127	37	-	-	PUNCT
ejpam-1375	127	38	fitting	fitting	NOUN
ejpam-1375	127	39	is	be	AUX
ejpam-1375	127	40	prevented	prevent	VERB
ejpam-1375	127	41	.	.	PUNCT
ejpam-1375	128	1	•	•	NUM
ejpam-1375	128	2	a	a	DET
ejpam-1375	128	3	combination	combination	NOUN
ejpam-1375	128	4	of	of	ADP
ejpam-1375	128	5	the	the	DET
ejpam-1375	128	6	two	two	NUM
ejpam-1375	128	7	:	:	PUNCT
ejpam-1375	128	8	two	two	NUM
ejpam-1375	128	9	forward	forward	ADJ
ejpam-1375	128	10	selection	selection	NOUN
ejpam-1375	128	11	steps	step	NOUN
ejpam-1375	128	12	followed	follow	VERB
ejpam-1375	128	13	by	by	ADP
ejpam-1375	128	14	one	one	NUM
ejpam-1375	128	15	backward	backward	ADJ
ejpam-1375	128	16	elimination	elimination	NOUN
ejpam-1375	128	17	step	step	NOUN
ejpam-1375	128	18	.	.	PUNCT
ejpam-1375	129	1	•	•	NOUN
ejpam-1375	129	2	all	all	DET
ejpam-1375	129	3	possible	possible	ADJ
ejpam-1375	129	4	subset	subset	NOUN
ejpam-1375	129	5	selection	selection	NOUN
ejpam-1375	129	6	:	:	PUNCT
ejpam-1375	129	7	full	full	ADJ
ejpam-1375	129	8	combinatorial	combinatorial	ADJ
ejpam-1375	129	9	search	search	NOUN
ejpam-1375	129	10	.	.	PUNCT
ejpam-1375	130	1	there	there	PRON
ejpam-1375	130	2	are	be	VERB
ejpam-1375	130	3	some	some	DET
ejpam-1375	130	4	problems	problem	NOUN
ejpam-1375	130	5	with	with	ADP
ejpam-1375	130	6	these	these	DET
ejpam-1375	130	7	techniques	technique	NOUN
ejpam-1375	130	8	.	.	PUNCT
ejpam-1375	131	1	both	both	PRON
ejpam-1375	131	2	forward	forward	ADJ
ejpam-1375	131	3	and	and	CCONJ
ejpam-1375	131	4	backward	backward	ADJ
ejpam-1375	131	5	procedures	procedure	NOUN
ejpam-1375	131	6	can	can	AUX
ejpam-1375	131	7	not	not	PART
ejpam-1375	131	8	deal	deal	VERB
ejpam-1375	131	9	with	with	ADP
ejpam-1375	131	10	the	the	DET
ejpam-1375	131	11	collinearity	collinearity	NOUN
ejpam-1375	131	12	in	in	ADP
ejpam-1375	131	13	the	the	DET
ejpam-1375	131	14	predictor	predictor	NOUN
ejpam-1375	131	15	variables	variable	NOUN
ejpam-1375	131	16	.	.	PUNCT
ejpam-1375	132	1	major	major	ADJ
ejpam-1375	132	2	criticisms	criticism	NOUN
ejpam-1375	132	3	on	on	ADP
ejpam-1375	132	4	the	the	DET
ejpam-1375	132	5	forward	forward	NOUN
ejpam-1375	132	6	,	,	PUNCT
ejpam-1375	132	7	backward	backward	ADV
ejpam-1375	132	8	,	,	PUNCT
ejpam-1375	132	9	and	and	CCONJ
ejpam-1375	132	10	stepwise	stepwise	ADJ
ejpam-1375	132	11	selection	selection	NOUN
ejpam-1375	132	12	are	be	AUX
ejpam-1375	132	13	that	that	SCONJ
ejpam-1375	132	14	,	,	PUNCT
ejpam-1375	132	15	little	little	ADJ
ejpam-1375	132	16	or	or	CCONJ
ejpam-1375	132	17	no	no	DET
ejpam-1375	132	18	theoretical	theoretical	ADJ
ejpam-1375	132	19	justification	justification	NOUN
ejpam-1375	132	20	exists	exist	VERB
ejpam-1375	132	21	for	for	ADP
ejpam-1375	132	22	the	the	DET
ejpam-1375	132	23	order	order	NOUN
ejpam-1375	132	24	in	in	ADP
ejpam-1375	132	25	which	which	PRON
ejpam-1375	132	26	variables	variable	NOUN
ejpam-1375	132	27	enter	enter	VERB
ejpam-1375	132	28	or	or	CCONJ
ejpam-1375	132	29	exit	exit	VERB
ejpam-1375	132	30	the	the	DET
ejpam-1375	132	31	algorithm	algorithm	NOUN
ejpam-1375	132	32	[	[	X
ejpam-1375	132	33	3	3	NUM
ejpam-1375	132	34	,	,	PUNCT
ejpam-1375	132	35	25	25	NUM
ejpam-1375	132	36	]	]	PUNCT
ejpam-1375	132	37	.	.	PUNCT
ejpam-1375	133	1	on	on	ADP
ejpam-1375	133	2	the	the	DET
ejpam-1375	133	3	other	other	ADJ
ejpam-1375	133	4	hand	hand	NOUN
ejpam-1375	133	5	,	,	PUNCT
ejpam-1375	133	6	stepwise	stepwise	NOUN
ejpam-1375	133	7	searching	searching	NOUN
ejpam-1375	133	8	rarely	rarely	ADV
ejpam-1375	133	9	finds	find	VERB
ejpam-1375	133	10	the	the	DET
ejpam-1375	133	11	overall	overall	ADJ
ejpam-1375	133	12	best	good	ADJ
ejpam-1375	133	13	model	model	NOUN
ejpam-1375	133	14	or	or	CCONJ
ejpam-1375	133	15	even	even	ADV
ejpam-1375	133	16	the	the	DET
ejpam-1375	133	17	best	good	ADJ
ejpam-1375	133	18	subsets	subset	NOUN
ejpam-1375	133	19	of	of	ADP
ejpam-1375	133	20	a	a	DET
ejpam-1375	133	21	particular	particular	ADJ
ejpam-1375	133	22	size	size	NOUN
ejpam-1375	133	23	[	[	X
ejpam-1375	133	24	17	17	NUM
ejpam-1375	133	25	,	,	PUNCT
ejpam-1375	133	26	10	10	NUM
ejpam-1375	133	27	,	,	PUNCT
ejpam-1375	133	28	18	18	NUM
ejpam-1375	133	29	]	]	PUNCT
ejpam-1375	133	30	.	.	PUNCT
ejpam-1375	134	1	stepwise	stepwise	PROPN
ejpam-1375	134	2	selection	selection	PROPN
ejpam-1375	134	3	,	,	PUNCT
ejpam-1375	134	4	at	at	ADP
ejpam-1375	134	5	the	the	DET
ejpam-1375	134	6	very	very	ADV
ejpam-1375	134	7	best	good	ADJ
ejpam-1375	134	8	,	,	PUNCT
ejpam-1375	134	9	can	can	AUX
ejpam-1375	134	10	only	only	ADV
ejpam-1375	134	11	produce	produce	VERB
ejpam-1375	134	12	an	an	DET
ejpam-1375	134	13	“	"	PUNCT
ejpam-1375	134	14	adequate	adequate	ADJ
ejpam-1375	134	15	”	"	PUNCT
ejpam-1375	134	16	model	model	NOUN
ejpam-1375	134	17	.	.	PUNCT
ejpam-1375	135	1	all	all	DET
ejpam-1375	135	2	possible	possible	ADJ
ejpam-1375	135	3	subset	subset	NOUN
ejpam-1375	135	4	selection	selection	NOUN
ejpam-1375	135	5	is	be	AUX
ejpam-1375	135	6	a	a	DET
ejpam-1375	135	7	fail	fail	ADJ
ejpam-1375	135	8	proof	proof	NOUN
ejpam-1375	135	9	method	method	NOUN
ejpam-1375	135	10	,	,	PUNCT
ejpam-1375	135	11	but	but	CCONJ
ejpam-1375	135	12	it	it	PRON
ejpam-1375	135	13	is	be	AUX
ejpam-1375	135	14	not	not	PART
ejpam-1375	135	15	computationally	computationally	ADV
ejpam-1375	135	16	feasible	feasible	ADJ
ejpam-1375	135	17	.	.	PUNCT
ejpam-1375	136	1	it	it	PRON
ejpam-1375	136	2	takes	take	VERB
ejpam-1375	136	3	too	too	ADV
ejpam-1375	136	4	much	much	ADJ
ejpam-1375	136	5	time	time	NOUN
ejpam-1375	136	6	to	to	PART
ejpam-1375	136	7	compute	compute	VERB
ejpam-1375	136	8	and	and	CCONJ
ejpam-1375	136	9	it	it	PRON
ejpam-1375	136	10	is	be	AUX
ejpam-1375	136	11	costly	costly	ADJ
ejpam-1375	136	12	.	.	PUNCT
ejpam-1375	137	1	for	for	ADP
ejpam-1375	137	2	20	20	NUM
ejpam-1375	137	3	predictor	predictor	NOUN
ejpam-1375	137	4	variables	variable	NOUN
ejpam-1375	137	5	,	,	PUNCT
ejpam-1375	137	6	for	for	ADP
ejpam-1375	137	7	the	the	DET
ejpam-1375	137	8	usual	usual	ADJ
ejpam-1375	137	9	subset	subset	NOUN
ejpam-1375	137	10	regression	regression	NOUN
ejpam-1375	137	11	model	model	NOUN
ejpam-1375	137	12	,	,	PUNCT
ejpam-1375	137	13	total	total	ADJ
ejpam-1375	137	14	number	number	NOUN
ejpam-1375	137	15	of	of	ADP
ejpam-1375	137	16	possible	possible	ADJ
ejpam-1375	137	17	models	model	NOUN
ejpam-1375	137	18	we	we	PRON
ejpam-1375	137	19	need	need	VERB
ejpam-1375	137	20	to	to	PART
ejpam-1375	137	21	evaluate	evaluate	VERB
ejpam-1375	137	22	is	be	AUX
ejpam-1375	137	23	:	:	PUNCT
ejpam-1375	137	24	220	220	NUM
ejpam-1375	137	25	=	=	SYM
ejpam-1375	137	26	1,048,576	1,048,576	NUM
ejpam-1375	137	27	.	.	PUNCT
ejpam-1375	138	1	the	the	DET
ejpam-1375	138	2	regression	regression	NOUN
ejpam-1375	138	3	trees	tree	NOUN
ejpam-1375	138	4	can	can	AUX
ejpam-1375	138	5	automatically	automatically	ADV
ejpam-1375	138	6	determine	determine	VERB
ejpam-1375	138	7	the	the	DET
ejpam-1375	138	8	relevance	relevance	NOUN
ejpam-1375	138	9	of	of	ADP
ejpam-1375	138	10	the	the	DET
ejpam-1375	138	11	variables	variable	NOUN
ejpam-1375	138	12	.	.	PUNCT
ejpam-1375	139	1	but	but	CCONJ
ejpam-1375	139	2	they	they	PRON
ejpam-1375	139	3	still	still	ADV
ejpam-1375	139	4	tend	tend	VERB
ejpam-1375	139	5	to	to	PART
ejpam-1375	139	6	overfit	overfit	VERB
ejpam-1375	139	7	the	the	DET
ejpam-1375	139	8	model	model	NOUN
ejpam-1375	139	9	because	because	SCONJ
ejpam-1375	139	10	the	the	DET
ejpam-1375	139	11	regression	regression	NOUN
ejpam-1375	139	12	tree	tree	NOUN
ejpam-1375	139	13	method	method	NOUN
ejpam-1375	139	14	does	do	AUX
ejpam-1375	139	15	not	not	PART
ejpam-1375	139	16	discard	discard	VERB
ejpam-1375	139	17	any	any	PRON
ejpam-1375	139	18	of	of	ADP
ejpam-1375	139	19	the	the	DET
ejpam-1375	139	20	predictor	predictor	NOUN
ejpam-1375	139	21	variables	variable	VERB
ejpam-1375	139	22	out	out	ADP
ejpam-1375	139	23	of	of	ADP
ejpam-1375	139	24	the	the	DET
ejpam-1375	139	25	models	model	NOUN
ejpam-1375	139	26	.	.	PUNCT
ejpam-1375	140	1	in	in	ADP
ejpam-1375	140	2	this	this	DET
ejpam-1375	140	3	case	case	NOUN
ejpam-1375	140	4	,	,	PUNCT
ejpam-1375	140	5	we	we	PRON
ejpam-1375	140	6	have	have	VERB
ejpam-1375	140	7	2220	2220	NUM
ejpam-1375	140	8	=	=	SYM
ejpam-1375	140	9	21,048,576	21,048,576	NUM
ejpam-1375	140	10	possible	possible	ADJ
ejpam-1375	140	11	models	model	NOUN
ejpam-1375	140	12	to	to	PART
ejpam-1375	140	13	evaluate	evaluate	VERB
ejpam-1375	140	14	and	and	CCONJ
ejpam-1375	140	15	to	to	PART
ejpam-1375	140	16	choose	choose	VERB
ejpam-1375	140	17	from	from	ADP
ejpam-1375	140	18	.	.	PUNCT
ejpam-1375	141	1	other	other	ADJ
ejpam-1375	141	2	major	major	ADJ
ejpam-1375	141	3	problems	problem	NOUN
ejpam-1375	141	4	of	of	ADP
ejpam-1375	141	5	these	these	DET
ejpam-1375	141	6	standard	standard	ADJ
ejpam-1375	141	7	techniques	technique	NOUN
ejpam-1375	141	8	are	be	AUX
ejpam-1375	141	9	,	,	PUNCT
ejpam-1375	141	10	over	over	ADV
ejpam-1375	141	11	-	-	PUNCT
ejpam-1375	141	12	fitting	fitting	ADJ
ejpam-1375	141	13	,	,	PUNCT
ejpam-1375	141	14	ill	ill	ADV
ejpam-1375	141	15	-	-	PUNCT
ejpam-1375	141	16	conditioned	condition	VERB
ejpam-1375	141	17	design	design	NOUN
ejpam-1375	141	18	matrix	matrix	NOUN
ejpam-1375	141	19	,	,	PUNCT
ejpam-1375	141	20	high	high	ADJ
ejpam-1375	141	21	collinearity	collinearity	NOUN
ejpam-1375	141	22	in	in	ADP
ejpam-1375	141	23	the	the	DET
ejpam-1375	141	24	predictor	predictor	NOUN
ejpam-1375	141	25	variables	variable	NOUN
ejpam-1375	141	26	and	and	CCONJ
ejpam-1375	141	27	,	,	PUNCT
ejpam-1375	141	28	computational	computational	ADJ
ejpam-1375	141	29	complexity	complexity	NOUN
ejpam-1375	141	30	,	,	PUNCT
ejpam-1375	141	31	etc	etc	X
ejpam-1375	141	32	.	.	X
ejpam-1375	142	1	in	in	ADP
ejpam-1375	142	2	this	this	DET
ejpam-1375	142	3	case	case	NOUN
ejpam-1375	142	4	what	what	PRON
ejpam-1375	142	5	we	we	PRON
ejpam-1375	142	6	need	need	VERB
ejpam-1375	142	7	is	be	AUX
ejpam-1375	142	8	an	an	DET
ejpam-1375	142	9	intelligent	intelligent	ADJ
ejpam-1375	142	10	hybrid	hybrid	NOUN
ejpam-1375	142	11	modeling	modeling	NOUN
ejpam-1375	142	12	between	between	ADP
ejpam-1375	142	13	:	:	PUNCT
ejpam-1375	142	14	•	•	NUM
ejpam-1375	142	15	any	any	DET
ejpam-1375	142	16	complex	complex	ADJ
ejpam-1375	142	17	modeling	modeling	NOUN
ejpam-1375	142	18	problems	problem	NOUN
ejpam-1375	142	19	such	such	ADJ
ejpam-1375	142	20	as	as	ADP
ejpam-1375	142	21	regression	regression	NOUN
ejpam-1375	142	22	trees	tree	NOUN
ejpam-1375	142	23	with	with	ADP
ejpam-1375	142	24	rbf	rbf	PROPN
ejpam-1375	142	25	-	-	PUNCT
ejpam-1375	142	26	nn	nn	PROPN
ejpam-1375	142	27	models	model	NOUN
ejpam-1375	142	28	.	.	PUNCT
ejpam-1375	143	1	•	•	NUM
ejpam-1375	143	2	a	a	DET
ejpam-1375	143	3	clever	clever	ADJ
ejpam-1375	143	4	model	model	NOUN
ejpam-1375	143	5	choice	choice	NOUN
ejpam-1375	143	6	criteria	criterion	NOUN
ejpam-1375	143	7	such	such	ADJ
ejpam-1375	143	8	as	as	ADP
ejpam-1375	143	9	the	the	DET
ejpam-1375	143	10	information	information	NOUN
ejpam-1375	143	11	complexity	complexity	NOUN
ejpam-1375	143	12	;	;	PUNCT
ejpam-1375	143	13	•	•	ADP
ejpam-1375	143	14	fast	fast	ADJ
ejpam-1375	143	15	and	and	CCONJ
ejpam-1375	143	16	efficient	efficient	ADJ
ejpam-1375	143	17	stochastic	stochastic	ADJ
ejpam-1375	143	18	search	search	NOUN
ejpam-1375	143	19	algorithms	algorithm	NOUN
ejpam-1375	143	20	such	such	ADJ
ejpam-1375	143	21	as	as	ADP
ejpam-1375	143	22	the	the	DET
ejpam-1375	143	23	genetic	genetic	ADJ
ejpam-1375	143	24	algorithms	algorithm	NOUN
ejpam-1375	143	25	(	(	PUNCT
ejpam-1375	143	26	ga	ga	NOUN
ejpam-1375	143	27	)	)	PUNCT
ejpam-1375	143	28	,	,	PUNCT
ejpam-1375	143	29	and	and	CCONJ
ejpam-1375	143	30	•	•	NUM
ejpam-1375	143	31	hybridization	hybridization	NOUN
ejpam-1375	143	32	of	of	ADP
ejpam-1375	143	33	ga	ga	PROPN
ejpam-1375	143	34	with	with	ADP
ejpam-1375	143	35	combinatorial	combinatorial	ADJ
ejpam-1375	143	36	all	all	DET
ejpam-1375	143	37	possible	possible	ADJ
ejpam-1375	143	38	subset	subset	NOUN
ejpam-1375	143	39	selection	selection	NOUN
ejpam-1375	143	40	.	.	PUNCT
ejpam-1375	144	1	o.	o.	PROPN
ejpam-1375	144	2	akbilgic	akbilgic	PROPN
ejpam-1375	144	3	,	,	PUNCT
ejpam-1375	144	4	h.	h.	PROPN
ejpam-1375	144	5	bozdogan	bozdogan	PROPN
ejpam-1375	144	6	/	/	SYM
ejpam-1375	144	7	eur	eur	PROPN
ejpam-1375	144	8	.	.	PUNCT
ejpam-1375	145	1	j.	j.	PROPN
ejpam-1375	145	2	pure	pure	PROPN
ejpam-1375	145	3	appl	appl	PROPN
ejpam-1375	145	4	.	.	PROPN
ejpam-1375	145	5	math	math	PROPN
ejpam-1375	145	6	,	,	PUNCT
ejpam-1375	145	7	4	4	NUM
ejpam-1375	145	8	(	(	PUNCT
ejpam-1375	145	9	2011	2011	NUM
ejpam-1375	145	10	)	)	PUNCT
ejpam-1375	145	11	,	,	PUNCT
ejpam-1375	145	12	467	467	NUM
ejpam-1375	145	13	-	-	SYM
ejpam-1375	145	14	485	485	NUM
ejpam-1375	145	15	473	473	NUM
ejpam-1375	145	16	5	5	NUM
ejpam-1375	145	17	.	.	PUNCT
ejpam-1375	146	1	regularization	regularization	NOUN
ejpam-1375	146	2	:	:	PUNCT
ejpam-1375	146	3	ridge	ridge	NOUN
ejpam-1375	146	4	regression	regression	NOUN
ejpam-1375	146	5	there	there	PRON
ejpam-1375	146	6	are	be	VERB
ejpam-1375	146	7	two	two	NUM
ejpam-1375	146	8	main	main	ADJ
ejpam-1375	146	9	ways	way	NOUN
ejpam-1375	146	10	to	to	PART
ejpam-1375	146	11	avoid	avoid	VERB
ejpam-1375	146	12	over	over	ADP
ejpam-1375	146	13	-	-	PUNCT
ejpam-1375	146	14	fitting	fit	VERB
ejpam-1375	146	15	and	and	CCONJ
ejpam-1375	146	16	to	to	PART
ejpam-1375	146	17	avoid	avoid	VERB
ejpam-1375	146	18	the	the	DET
ejpam-1375	146	19	potential	potential	ADJ
ejpam-1375	146	20	singularities	singularity	NOUN
ejpam-1375	146	21	in	in	ADP
ejpam-1375	146	22	the	the	DET
ejpam-1375	146	23	design	design	NOUN
ejpam-1375	146	24	matrix	matrix	NOUN
ejpam-1375	146	25	.	.	PUNCT
ejpam-1375	147	1	the	the	DET
ejpam-1375	147	2	first	first	ADJ
ejpam-1375	147	3	way	way	NOUN
ejpam-1375	147	4	,	,	PUNCT
ejpam-1375	147	5	regularization	regularization	NOUN
ejpam-1375	147	6	[	[	X
ejpam-1375	147	7	23	23	NUM
ejpam-1375	147	8	,	,	PUNCT
ejpam-1375	147	9	2	2	NUM
ejpam-1375	147	10	]	]	PUNCT
ejpam-1375	147	11	,	,	PUNCT
ejpam-1375	147	12	reduces	reduce	VERB
ejpam-1375	147	13	the	the	DET
ejpam-1375	147	14	“	"	PUNCT
ejpam-1375	147	15	number	number	NOUN
ejpam-1375	147	16	of	of	ADP
ejpam-1375	147	17	good	good	ADJ
ejpam-1375	147	18	parameter	parameter	NOUN
ejpam-1375	147	19	measurements	measurement	NOUN
ejpam-1375	147	20	”	"	PUNCT
ejpam-1375	148	1	[	[	X
ejpam-1375	148	2	16	16	NUM
ejpam-1375	148	3	]	]	PUNCT
ejpam-1375	148	4	in	in	ADP
ejpam-1375	148	5	a	a	DET
ejpam-1375	148	6	large	large	ADJ
ejpam-1375	148	7	full	full	ADJ
ejpam-1375	148	8	saturated	saturated	ADJ
ejpam-1375	148	9	model	model	NOUN
ejpam-1375	148	10	by	by	ADP
ejpam-1375	148	11	adding	add	VERB
ejpam-1375	148	12	a	a	DET
ejpam-1375	148	13	weight	weight	NOUN
ejpam-1375	148	14	penalty	penalty	NOUN
ejpam-1375	148	15	term	term	NOUN
ejpam-1375	148	16	to	to	ADP
ejpam-1375	148	17	the	the	DET
ejpam-1375	148	18	minimization	minimization	NOUN
ejpam-1375	148	19	criterion	criterion	NOUN
ejpam-1375	148	20	.	.	PUNCT
ejpam-1375	149	1	we	we	PRON
ejpam-1375	149	2	introduce	introduce	VERB
ejpam-1375	149	3	the	the	DET
ejpam-1375	149	4	regularization	regularization	NOUN
ejpam-1375	149	5	by	by	ADP
ejpam-1375	149	6	using	use	VERB
ejpam-1375	149	7	global	global	ADJ
ejpam-1375	149	8	ridge	ridge	NOUN
ejpam-1375	149	9	regressions	regression	NOUN
ejpam-1375	149	10	to	to	PART
ejpam-1375	149	11	avoid	avoid	VERB
ejpam-1375	149	12	the	the	DET
ejpam-1375	149	13	potential	potential	ADJ
ejpam-1375	149	14	singularities	singularity	NOUN
ejpam-1375	149	15	in	in	ADP
ejpam-1375	149	16	the	the	DET
ejpam-1375	149	17	model	model	NOUN
ejpam-1375	149	18	matrix	matrix	NOUN
ejpam-1375	149	19	.	.	PUNCT
ejpam-1375	150	1	second	second	ADJ
ejpam-1375	150	2	way	way	NOUN
ejpam-1375	150	3	to	to	PART
ejpam-1375	150	4	avoid	avoid	VERB
ejpam-1375	150	5	over	over	ADP
ejpam-1375	150	6	-	-	PUNCT
ejpam-1375	150	7	fitting	fitting	NOUN
ejpam-1375	150	8	is	be	AUX
ejpam-1375	150	9	to	to	PART
ejpam-1375	150	10	explicitly	explicitly	ADV
ejpam-1375	150	11	limit	limit	VERB
ejpam-1375	150	12	the	the	DET
ejpam-1375	150	13	complexity	complexity	NOUN
ejpam-1375	150	14	of	of	ADP
ejpam-1375	150	15	the	the	DET
ejpam-1375	150	16	network	network	NOUN
ejpam-1375	150	17	by	by	ADP
ejpam-1375	150	18	allowing	allow	VERB
ejpam-1375	150	19	only	only	ADV
ejpam-1375	150	20	a	a	DET
ejpam-1375	150	21	subset	subset	NOUN
ejpam-1375	150	22	of	of	ADP
ejpam-1375	150	23	the	the	DET
ejpam-1375	150	24	variables	variable	NOUN
ejpam-1375	150	25	using	use	VERB
ejpam-1375	150	26	information	information	NOUN
ejpam-1375	150	27	criteria	criterion	NOUN
ejpam-1375	150	28	to	to	PART
ejpam-1375	150	29	determine	determine	VERB
ejpam-1375	150	30	the	the	DET
ejpam-1375	150	31	parsimonious	parsimonious	ADJ
ejpam-1375	150	32	networks	network	NOUN
ejpam-1375	150	33	and	and	CCONJ
ejpam-1375	150	34	best	good	ADJ
ejpam-1375	150	35	subset	subset	NOUN
ejpam-1375	150	36	of	of	ADP
ejpam-1375	150	37	predictors	predictor	NOUN
ejpam-1375	150	38	.	.	PUNCT
ejpam-1375	151	1	in	in	ADP
ejpam-1375	151	2	this	this	DET
ejpam-1375	151	3	paper	paper	NOUN
ejpam-1375	151	4	,	,	PUNCT
ejpam-1375	151	5	we	we	PRON
ejpam-1375	151	6	use	use	VERB
ejpam-1375	151	7	not	not	PART
ejpam-1375	151	8	only	only	ADV
ejpam-1375	151	9	ridge	ridge	NOUN
ejpam-1375	151	10	regression	regression	NOUN
ejpam-1375	151	11	but	but	CCONJ
ejpam-1375	151	12	also	also	ADV
ejpam-1375	151	13	subset	subset	VERB
ejpam-1375	151	14	selection	selection	NOUN
ejpam-1375	151	15	to	to	PART
ejpam-1375	151	16	avoid	avoid	VERB
ejpam-1375	151	17	over	over	ADP
ejpam-1375	151	18	-	-	PUNCT
ejpam-1375	151	19	fitting	fit	VERB
ejpam-1375	151	20	and	and	CCONJ
ejpam-1375	151	21	singularity	singularity	NOUN
ejpam-1375	151	22	problems	problem	NOUN
ejpam-1375	151	23	.	.	PUNCT
ejpam-1375	152	1	5.1	5.1	NUM
ejpam-1375	152	2	.	.	PUNCT
ejpam-1375	152	3	global	global	PROPN
ejpam-1375	152	4	ridge	ridge	PROPN
ejpam-1375	152	5	regression	regression	PROPN
ejpam-1375	152	6	in	in	ADP
ejpam-1375	152	7	the	the	DET
ejpam-1375	152	8	global	global	PROPN
ejpam-1375	152	9	ridge	ridge	PROPN
ejpam-1375	152	10	regression	regression	PROPN
ejpam-1375	152	11	to	to	PART
ejpam-1375	152	12	counter	counter	VERB
ejpam-1375	152	13	the	the	DET
ejpam-1375	152	14	effects	effect	NOUN
ejpam-1375	152	15	of	of	ADP
ejpam-1375	152	16	over	over	ADV
ejpam-1375	152	17	-	-	PUNCT
ejpam-1375	152	18	fitting	fitting	NOUN
ejpam-1375	152	19	,	,	PUNCT
ejpam-1375	152	20	a	a	DET
ejpam-1375	152	21	roughness	roughness	NOUN
ejpam-1375	152	22	penalty	penalty	NOUN
ejpam-1375	152	23	term	term	NOUN
ejpam-1375	152	24	is	be	AUX
ejpam-1375	152	25	added	add	VERB
ejpam-1375	152	26	to	to	ADP
ejpam-1375	152	27	the	the	DET
ejpam-1375	152	28	sum	sum	NOUN
ejpam-1375	152	29	of	of	ADP
ejpam-1375	152	30	squared	square	VERB
ejpam-1375	152	31	errors	error	NOUN
ejpam-1375	152	32	to	to	PART
ejpam-1375	152	33	produce	produce	VERB
ejpam-1375	152	34	the	the	DET
ejpam-1375	152	35	cost	cost	NOUN
ejpam-1375	152	36	function	function	NOUN
ejpam-1375	152	37	;	;	PUNCT
ejpam-1375	152	38	c(w	c(w	PROPN
ejpam-1375	152	39	,	,	PUNCT
ejpam-1375	152	40	λ	λ	NOUN
ejpam-1375	152	41	)	)	PUNCT
ejpam-1375	152	42	=	=	PUNCT
ejpam-1375	153	1	p∑	p∑	X
ejpam-1375	154	1	i=1	i=1	PROPN
ejpam-1375	154	2	�	�	PROPN
ejpam-1375	155	1	f	f	PROPN
ejpam-1375	155	2	(	(	PUNCT
ejpam-1375	155	3	x	x	PROPN
ejpam-1375	155	4	i)−	i)−	PROPN
ejpam-1375	155	5	yi	yi	NOUN
ejpam-1375	155	6	�	�	PROPN
ejpam-1375	155	7	2	2	NUM
ejpam-1375	155	8	+	+	NOUN
ejpam-1375	155	9	λ	λ	NOUN
ejpam-1375	155	10	m∑	m∑	NOUN
ejpam-1375	155	11	i=1	i=1	PROPN
ejpam-1375	155	12	w2	w2	PROPN
ejpam-1375	155	13	j	j	PROPN
ejpam-1375	156	1	=	=	PUNCT
ejpam-1375	156	2	ǫ	ǫ	X
ejpam-1375	156	3	′ǫ+w′w	′ǫ+w′w	PRON
ejpam-1375	156	4	(	(	PUNCT
ejpam-1375	156	5	18	18	NUM
ejpam-1375	156	6	)	)	PUNCT
ejpam-1375	156	7	which	which	PRON
ejpam-1375	156	8	is	be	AUX
ejpam-1375	156	9	minimized	minimize	VERB
ejpam-1375	156	10	to	to	PART
ejpam-1375	156	11	find	find	VERB
ejpam-1375	156	12	a	a	DET
ejpam-1375	156	13	weight	weight	NOUN
ejpam-1375	156	14	vector	vector	NOUN
ejpam-1375	156	15	which	which	PRON
ejpam-1375	156	16	is	be	AUX
ejpam-1375	156	17	more	more	ADV
ejpam-1375	156	18	robust	robust	ADJ
ejpam-1375	156	19	to	to	PART
ejpam-1375	156	20	noise	noise	VERB
ejpam-1375	156	21	in	in	ADP
ejpam-1375	156	22	the	the	DET
ejpam-1375	156	23	training	training	NOUN
ejpam-1375	156	24	set	set	NOUN
ejpam-1375	156	25	.	.	PUNCT
ejpam-1375	157	1	the	the	DET
ejpam-1375	157	2	optimal	optimal	ADJ
ejpam-1375	157	3	weight	weight	NOUN
ejpam-1375	157	4	vector	vector	NOUN
ejpam-1375	157	5	for	for	ADP
ejpam-1375	157	6	global	global	PROPN
ejpam-1375	157	7	ridge	ridge	PROPN
ejpam-1375	157	8	regression	regression	NOUN
ejpam-1375	157	9	is	be	AUX
ejpam-1375	157	10	ŵ	ŵ	X
ejpam-1375	157	11	=	=	SYM
ejpam-1375	157	12	�	�	PROPN
ejpam-1375	157	13	h	h	NOUN
ejpam-1375	158	1	′	′	NUM
ejpam-1375	158	2	h	h	NOUN
ejpam-1375	159	1	+	+	ADJ
ejpam-1375	159	2	λim	λim	ADJ
ejpam-1375	159	3	�	�	NOUN
ejpam-1375	159	4	−1	−1	NOUN
ejpam-1375	159	5	h	h	NOUN
ejpam-1375	159	6	′	′	NUM
ejpam-1375	160	1	y	y	PROPN
ejpam-1375	160	2	(	(	PUNCT
ejpam-1375	160	3	19	19	NUM
ejpam-1375	160	4	)	)	PUNCT
ejpam-1375	160	5	where	where	SCONJ
ejpam-1375	160	6	i	i	PRON
ejpam-1375	160	7	m	m	VERB
ejpam-1375	160	8	is	be	AUX
ejpam-1375	160	9	the	the	DET
ejpam-1375	160	10	m	m	ADJ
ejpam-1375	160	11	dimensional	dimensional	ADJ
ejpam-1375	160	12	identity	identity	NOUN
ejpam-1375	160	13	matrix	matrix	NOUN
ejpam-1375	160	14	.	.	PUNCT
ejpam-1375	161	1	5.2	5.2	NUM
ejpam-1375	161	2	.	.	PUNCT
ejpam-1375	161	3	local	local	PROPN
ejpam-1375	161	4	ridge	ridge	PROPN
ejpam-1375	161	5	regression	regression	NOUN
ejpam-1375	161	6	we	we	PRON
ejpam-1375	161	7	generalize	generalize	VERB
ejpam-1375	161	8	the	the	DET
ejpam-1375	161	9	global	global	PROPN
ejpam-1375	161	10	ridge	ridge	PROPN
ejpam-1375	161	11	regression	regression	NOUN
ejpam-1375	161	12	to	to	PART
ejpam-1375	161	13	attach	attach	VERB
ejpam-1375	161	14	a	a	DET
ejpam-1375	161	15	separate	separate	ADJ
ejpam-1375	161	16	regularization	regularization	NOUN
ejpam-1375	161	17	parameter	parameter	NOUN
ejpam-1375	161	18	to	to	ADP
ejpam-1375	161	19	each	each	DET
ejpam-1375	161	20	basis	basis	NOUN
ejpam-1375	161	21	function	function	NOUN
ejpam-1375	161	22	by	by	ADP
ejpam-1375	161	23	using	use	VERB
ejpam-1375	161	24	the	the	DET
ejpam-1375	161	25	cost	cost	NOUN
ejpam-1375	161	26	function	function	NOUN
ejpam-1375	161	27	c(w	c(w	PROPN
ejpam-1375	161	28	,	,	PUNCT
ejpam-1375	161	29	λ	λ	NOUN
ejpam-1375	161	30	)	)	PUNCT
ejpam-1375	161	31	=	=	PUNCT
ejpam-1375	162	1	p∑	p∑	X
ejpam-1375	163	1	i=1	i=1	PROPN
ejpam-1375	163	2	�	�	PROPN
ejpam-1375	164	1	f	f	PROPN
ejpam-1375	164	2	(	(	PUNCT
ejpam-1375	164	3	x	x	PROPN
ejpam-1375	164	4	i)−	i)−	PROPN
ejpam-1375	164	5	yi	yi	NOUN
ejpam-1375	164	6	�	�	PROPN
ejpam-1375	164	7	2	2	NUM
ejpam-1375	164	8	+	+	CCONJ
ejpam-1375	164	9	m∑	m∑	PROPN
ejpam-1375	164	10	i=1	i=1	PROPN
ejpam-1375	165	1	λ	λ	PROPN
ejpam-1375	165	2	jw	jw	PROPN
ejpam-1375	165	3	2	2	NUM
ejpam-1375	165	4	j	j	PROPN
ejpam-1375	165	5	.	.	PUNCT
ejpam-1375	166	1	(	(	PUNCT
ejpam-1375	166	2	20	20	NUM
ejpam-1375	166	3	)	)	PUNCT
ejpam-1375	166	4	leading	lead	VERB
ejpam-1375	166	5	to	to	ADP
ejpam-1375	166	6	the	the	DET
ejpam-1375	166	7	optimal	optimal	ADJ
ejpam-1375	166	8	weight	weight	NOUN
ejpam-1375	166	9	of	of	ADP
ejpam-1375	166	10	ŵ	ŵ	X
ejpam-1375	166	11	=	=	SYM
ejpam-1375	166	12	�	�	PROPN
ejpam-1375	166	13	h	h	NOUN
ejpam-1375	166	14	′	′	NUM
ejpam-1375	166	15	h	h	NOUN
ejpam-1375	167	1	+	+	NOUN
ejpam-1375	167	2	λ	λ	PROPN
ejpam-1375	167	3	�	�	PROPN
ejpam-1375	167	4	−1	−1	NOUN
ejpam-1375	167	5	h	h	NOUN
ejpam-1375	168	1	′	′	NUM
ejpam-1375	168	2	y	y	PROPN
ejpam-1375	168	3	,	,	PUNCT
ejpam-1375	168	4	(	(	PUNCT
ejpam-1375	168	5	21	21	NUM
ejpam-1375	168	6	)	)	PUNCT
ejpam-1375	168	7	where	where	SCONJ
ejpam-1375	168	8	λ	λ	X
ejpam-1375	168	9	=	=	SYM
ejpam-1375	168	10	diag{λ	diag{λ	NOUN
ejpam-1375	168	11	j}mj=1	j}mj=1	NOUN
ejpam-1375	168	12	is	be	AUX
ejpam-1375	168	13	a	a	DET
ejpam-1375	168	14	diagonal	diagonal	ADJ
ejpam-1375	168	15	regularization	regularization	NOUN
ejpam-1375	168	16	parameter	parameter	NOUN
ejpam-1375	168	17	matrix	matrix	NOUN
ejpam-1375	168	18	.	.	PUNCT
ejpam-1375	169	1	5.3	5.3	NUM
ejpam-1375	169	2	.	.	PUNCT
ejpam-1375	169	3	choosing	choose	VERB
ejpam-1375	169	4	the	the	DET
ejpam-1375	169	5	optimal	optimal	ADJ
ejpam-1375	169	6	ridge	ridge	NOUN
ejpam-1375	169	7	parameter	parameter	NOUN
ejpam-1375	169	8	there	there	PRON
ejpam-1375	169	9	is	be	VERB
ejpam-1375	169	10	much	much	ADJ
ejpam-1375	169	11	controversy	controversy	NOUN
ejpam-1375	169	12	as	as	ADP
ejpam-1375	169	13	to	to	ADP
ejpam-1375	169	14	how	how	SCONJ
ejpam-1375	169	15	to	to	PART
ejpam-1375	169	16	choose	choose	VERB
ejpam-1375	169	17	the	the	DET
ejpam-1375	169	18	ridge	ridge	NOUN
ejpam-1375	169	19	parameter	parameter	PROPN
ejpam-1375	169	20	λ	λ	PROPN
ejpam-1375	169	21	.	.	PUNCT
ejpam-1375	170	1	several	several	ADJ
ejpam-1375	170	2	authors	author	NOUN
ejpam-1375	170	3	have	have	AUX
ejpam-1375	170	4	proposed	propose	VERB
ejpam-1375	170	5	analytical	analytical	ADJ
ejpam-1375	170	6	procedures	procedure	NOUN
ejpam-1375	170	7	for	for	ADP
ejpam-1375	170	8	choosing	choose	VERB
ejpam-1375	170	9	the	the	DET
ejpam-1375	170	10	optimal	optimal	ADJ
ejpam-1375	170	11	parameter	parameter	NOUN
ejpam-1375	170	12	λ	λ	PROPN
ejpam-1375	170	13	.	.	PUNCT
ejpam-1375	171	1	some	some	PRON
ejpam-1375	171	2	of	of	ADP
ejpam-1375	171	3	these	these	PRON
ejpam-1375	171	4	are	be	AUX
ejpam-1375	171	5	:	:	PUNCT
ejpam-1375	171	6	o.	o.	PROPN
ejpam-1375	171	7	akbilgic	akbilgic	PROPN
ejpam-1375	171	8	,	,	PUNCT
ejpam-1375	171	9	h.	h.	PROPN
ejpam-1375	171	10	bozdogan	bozdogan	PROPN
ejpam-1375	171	11	/	/	SYM
ejpam-1375	171	12	eur	eur	PROPN
ejpam-1375	171	13	.	.	PUNCT
ejpam-1375	172	1	j.	j.	PROPN
ejpam-1375	172	2	pure	pure	PROPN
ejpam-1375	172	3	appl	appl	PROPN
ejpam-1375	172	4	.	.	PROPN
ejpam-1375	172	5	math	math	PROPN
ejpam-1375	172	6	,	,	PUNCT
ejpam-1375	172	7	4	4	NUM
ejpam-1375	172	8	(	(	PUNCT
ejpam-1375	172	9	2011	2011	NUM
ejpam-1375	172	10	)	)	PUNCT
ejpam-1375	172	11	,	,	PUNCT
ejpam-1375	172	12	467	467	NUM
ejpam-1375	172	13	-	-	SYM
ejpam-1375	172	14	485	485	NUM
ejpam-1375	172	15	474	474	NUM
ejpam-1375	172	16	•	•	NUM
ejpam-1375	172	17	hoerl	hoerl	NOUN
ejpam-1375	172	18	,	,	PUNCT
ejpam-1375	172	19	kennard	kennard	PROPN
ejpam-1375	172	20	&	&	CCONJ
ejpam-1375	172	21	baldwin	baldwin	PROPN
ejpam-1375	172	22	(	(	PUNCT
ejpam-1375	172	23	hkb	hkb	PROPN
ejpam-1375	172	24	)	)	PUNCT
ejpam-1375	173	1	[	[	X
ejpam-1375	173	2	11	11	NUM
ejpam-1375	173	3	]	]	PUNCT
ejpam-1375	173	4	approach	approach	NOUN
ejpam-1375	173	5	to	to	ADP
ejpam-1375	173	6	choosing	choose	VERB
ejpam-1375	173	7	λ	λ	X
ejpam-1375	173	8	λ̂hkb	λ̂hkb	NOUN
ejpam-1375	173	9	=	=	SYM
ejpam-1375	173	10	ms2	ms2	NOUN
ejpam-1375	173	11	ŵ	ŵ	PROPN
ejpam-1375	173	12	′	′	NUM
ejpam-1375	174	1	lsŵls	lsŵls	PROPN
ejpam-1375	174	2	(	(	PUNCT
ejpam-1375	174	3	22	22	NUM
ejpam-1375	174	4	)	)	PUNCT
ejpam-1375	174	5	where	where	SCONJ
ejpam-1375	174	6	m=	m=	X
ejpam-1375	174	7	k	k	NOUN
ejpam-1375	174	8	,	,	PUNCT
ejpam-1375	174	9	the	the	DET
ejpam-1375	174	10	number	number	NOUN
ejpam-1375	174	11	of	of	ADP
ejpam-1375	174	12	predictors	predictor	NOUN
ejpam-1375	174	13	not	not	PART
ejpam-1375	174	14	including	include	VERB
ejpam-1375	174	15	the	the	DET
ejpam-1375	174	16	intercept	intercept	NOUN
ejpam-1375	174	17	term	term	NOUN
ejpam-1375	174	18	,	,	PUNCT
ejpam-1375	174	19	n	n	X
ejpam-1375	174	20	is	be	AUX
ejpam-1375	174	21	the	the	DET
ejpam-1375	174	22	number	number	NOUN
ejpam-1375	174	23	of	of	ADP
ejpam-1375	174	24	observations	observation	NOUN
ejpam-1375	174	25	,	,	PUNCT
ejpam-1375	174	26	s2	s2	PROPN
ejpam-1375	174	27	is	be	AUX
ejpam-1375	174	28	the	the	DET
ejpam-1375	174	29	estimated	estimate	VERB
ejpam-1375	174	30	error	error	NOUN
ejpam-1375	174	31	variance	variance	NOUN
ejpam-1375	174	32	using	use	VERB
ejpam-1375	174	33	k	k	PROPN
ejpam-1375	174	34	predictors	predictor	NOUN
ejpam-1375	174	35	so	so	SCONJ
ejpam-1375	174	36	that	that	SCONJ
ejpam-1375	174	37	s2	s2	NOUN
ejpam-1375	174	38	=	=	NOUN
ejpam-1375	174	39	1	1	NUM
ejpam-1375	174	40	(	(	PUNCT
ejpam-1375	174	41	n−	n−	NOUN
ejpam-1375	174	42	k+	k+	NOUN
ejpam-1375	174	43	1	1	NUM
ejpam-1375	174	44	)	)	PUNCT
ejpam-1375	174	45	�	�	PROPN
ejpam-1375	174	46	y	y	PROPN
ejpam-1375	174	47	−hŵls	−hŵls	PROPN
ejpam-1375	174	48	�	�	PROPN
ejpam-1375	174	49	′	′	NUM
ejpam-1375	174	50	�	�	PROPN
ejpam-1375	174	51	y	y	PROPN
ejpam-1375	174	52	−hŵls	−hŵls	PROPN
ejpam-1375	174	53	�	�	PROPN
ejpam-1375	174	54	(	(	PUNCT
ejpam-1375	174	55	23	23	NUM
ejpam-1375	174	56	)	)	PUNCT
ejpam-1375	174	57	and	and	CCONJ
ejpam-1375	174	58	ŵls	ŵls	PROPN
ejpam-1375	174	59	is	be	AUX
ejpam-1375	174	60	the	the	DET
ejpam-1375	174	61	estimated	estimate	VERB
ejpam-1375	174	62	coefficient	coefficient	NOUN
ejpam-1375	174	63	vector	vector	NOUN
ejpam-1375	174	64	obtained	obtain	VERB
ejpam-1375	174	65	from	from	ADP
ejpam-1375	174	66	a	a	DET
ejpam-1375	174	67	no	no	PRON
ejpam-1375	174	68	-	-	PUNCT
ejpam-1375	174	69	constant	constant	ADJ
ejpam-1375	174	70	model	model	NOUN
ejpam-1375	174	71	given	give	VERB
ejpam-1375	174	72	by	by	ADP
ejpam-1375	174	73	ŵls	ŵls	PROPN
ejpam-1375	174	74	=	=	SYM
ejpam-1375	174	75	�	�	PROPN
ejpam-1375	174	76	h	h	NOUN
ejpam-1375	174	77	′h	′h	PROPN
ejpam-1375	174	78	�	�	PROPN
ejpam-1375	174	79	−1	−1	NOUN
ejpam-1375	174	80	h	h	NOUN
ejpam-1375	175	1	′	′	NUM
ejpam-1375	176	1	y.	y.	NOUN
ejpam-1375	176	2	(	(	PUNCT
ejpam-1375	176	3	24	24	NUM
ejpam-1375	176	4	)	)	PUNCT
ejpam-1375	176	5	•	•	NOUN
ejpam-1375	176	6	lawless	lawless	ADJ
ejpam-1375	176	7	and	and	CCONJ
ejpam-1375	176	8	wang	wang	PROPN
ejpam-1375	176	9	[	[	X
ejpam-1375	176	10	14	14	NUM
ejpam-1375	176	11	]	]	PUNCT
ejpam-1375	176	12	suggested	suggest	VERB
ejpam-1375	177	1	that	that	SCONJ
ejpam-1375	177	2	ŵls	ŵls	PROPN
ejpam-1375	177	3	=	=	PROPN
ejpam-1375	177	4	ms2	ms2	PROPN
ejpam-1375	177	5	∑k	∑k	PROPN
ejpam-1375	178	1	j=1	j=1	PROPN
ejpam-1375	178	2	ŵ2	ŵ2	PROPN
ejpam-1375	178	3	j	j	PROPN
ejpam-1375	178	4	λ	λ	X
ejpam-1375	178	5	j	j	PROPN
ejpam-1375	178	6	(	(	PUNCT
ejpam-1375	178	7	25	25	NUM
ejpam-1375	178	8	)	)	PUNCT
ejpam-1375	178	9	as	as	ADP
ejpam-1375	178	10	an	an	DET
ejpam-1375	178	11	estimator	estimator	NOUN
ejpam-1375	178	12	of	of	ADP
ejpam-1375	178	13	σ̂2	σ̂2	PROPN
ejpam-1375	178	14	/	/	SYM
ejpam-1375	178	15	σ̂2	σ̂2	PROPN
ejpam-1375	178	16	w	w	PROPN
ejpam-1375	178	17	based	base	VERB
ejpam-1375	178	18	on	on	ADP
ejpam-1375	178	19	bayesian	bayesian	NOUN
ejpam-1375	178	20	argument	argument	NOUN
ejpam-1375	178	21	.	.	PUNCT
ejpam-1375	179	1	•	•	NUM
ejpam-1375	179	2	empirical	empirical	ADJ
ejpam-1375	179	3	bayes	bayes	NOUN
ejpam-1375	179	4	method	method	NOUN
ejpam-1375	179	5	of	of	ADP
ejpam-1375	179	6	determining	determine	VERB
ejpam-1375	179	7	λ	λ	PROPN
ejpam-1375	179	8	proposed	propose	VERB
ejpam-1375	179	9	by	by	ADP
ejpam-1375	179	10	sclove	sclove	NOUN
ejpam-1375	180	1	[	[	X
ejpam-1375	180	2	22	22	NUM
ejpam-1375	180	3	]	]	SYM
ejpam-1375	180	4	λ̂s	λ̂s	NOUN
ejpam-1375	180	5	=	=	PUNCT
ejpam-1375	180	6	σ̂2	σ̂2	NOUN
ejpam-1375	180	7	σ̂2	σ̂2	PROPN
ejpam-1375	180	8	w	w	PROPN
ejpam-1375	180	9	(	(	PUNCT
ejpam-1375	180	10	26	26	NUM
ejpam-1375	180	11	)	)	PUNCT
ejpam-1375	180	12	where	where	SCONJ
ejpam-1375	180	13	σ̂2	σ̂2	PROPN
ejpam-1375	180	14	=	=	SYM
ejpam-1375	180	15	1	1	NUM
ejpam-1375	180	16	n	n	NUM
ejpam-1375	180	17	y	y	NOUN
ejpam-1375	180	18	′	′	NUM
ejpam-1375	181	1	h	h	NOUN
ejpam-1375	182	1	i	i	PRON
ejpam-1375	182	2	−h	−h	VERB
ejpam-1375	182	3	�	�	PROPN
ejpam-1375	182	4	h	h	NOUN
ejpam-1375	182	5	′h	′h	PROPN
ejpam-1375	182	6	�	�	PROPN
ejpam-1375	182	7	−1	−1	NOUN
ejpam-1375	182	8	h	h	NOUN
ejpam-1375	183	1	′	′	VERB
ejpam-1375	184	1	i	i	PRON
ejpam-1375	184	2	y	y	PROPN
ejpam-1375	184	3	(	(	PUNCT
ejpam-1375	184	4	27	27	NUM
ejpam-1375	184	5	)	)	PUNCT
ejpam-1375	184	6	is	be	AUX
ejpam-1375	184	7	the	the	DET
ejpam-1375	184	8	estimated	estimate	VERB
ejpam-1375	184	9	residual	residual	ADJ
ejpam-1375	184	10	variance	variance	NOUN
ejpam-1375	184	11	and	and	CCONJ
ejpam-1375	184	12	σ̂2	σ̂2	NOUN
ejpam-1375	184	13	w	w	PROPN
ejpam-1375	185	1	=	=	SYM
ejpam-1375	185	2	y	y	PROPN
ejpam-1375	185	3	′	′	NUM
ejpam-1375	186	1	y	y	NOUN
ejpam-1375	186	2	−	−	PROPN
ejpam-1375	186	3	nσ̂2	nσ̂2	PROPN
ejpam-1375	186	4	t	t	PROPN
ejpam-1375	186	5	r	r	NOUN
ejpam-1375	186	6	(	(	PUNCT
ejpam-1375	186	7	h	h	NOUN
ejpam-1375	186	8	′h	′h	NOUN
ejpam-1375	186	9	)	)	PUNCT
ejpam-1375	186	10	.	.	PUNCT
ejpam-1375	187	1	(	(	PUNCT
ejpam-1375	187	2	28	28	NUM
ejpam-1375	187	3	)	)	PUNCT
ejpam-1375	187	4	6	6	NUM
ejpam-1375	187	5	.	.	PUNCT
ejpam-1375	187	6	information	information	NOUN
ejpam-1375	187	7	theoretic	theoretic	NOUN
ejpam-1375	187	8	model	model	NOUN
ejpam-1375	187	9	selection	selection	NOUN
ejpam-1375	187	10	criteria	criterion	NOUN
ejpam-1375	187	11	for	for	ADP
ejpam-1375	187	12	the	the	DET
ejpam-1375	187	13	model	model	NOUN
ejpam-1375	187	14	seletion	seletion	NOUN
ejpam-1375	187	15	,	,	PUNCT
ejpam-1375	187	16	we	we	PRON
ejpam-1375	187	17	use	use	VERB
ejpam-1375	187	18	information	information	NOUN
ejpam-1375	187	19	theoretic	theoretic	NOUN
ejpam-1375	187	20	measure	measure	NOUN
ejpam-1375	187	21	of	of	ADP
ejpam-1375	187	22	complexity	complexity	NOUN
ejpam-1375	187	23	(	(	PUNCT
ejpam-1375	187	24	icomp	icomp	ADJ
ejpam-1375	187	25	)	)	PUNCT
ejpam-1375	187	26	criteria	criterion	NOUN
ejpam-1375	187	27	of	of	ADP
ejpam-1375	187	28	[	[	X
ejpam-1375	187	29	5	5	NUM
ejpam-1375	187	30	,	,	PUNCT
ejpam-1375	187	31	6	6	NUM
ejpam-1375	187	32	,	,	PUNCT
ejpam-1375	187	33	7	7	NUM
ejpam-1375	187	34	,	,	PUNCT
ejpam-1375	187	35	8	8	NUM
ejpam-1375	187	36	]	]	PUNCT
ejpam-1375	187	37	function	function	NOUN
ejpam-1375	187	38	to	to	PART
ejpam-1375	187	39	choose	choose	VERB
ejpam-1375	187	40	the	the	DET
ejpam-1375	187	41	best	good	ADJ
ejpam-1375	187	42	fitting	fitting	ADJ
ejpam-1375	187	43	basis	basis	NOUN
ejpam-1375	187	44	kernel	kernel	NOUN
ejpam-1375	187	45	rbfs	rbfs	NOUN
ejpam-1375	187	46	,	,	PUNCT
ejpam-1375	187	47	and	and	CCONJ
ejpam-1375	187	48	the	the	DET
ejpam-1375	187	49	best	good	ADJ
ejpam-1375	187	50	subset	subset	NOUN
ejpam-1375	187	51	of	of	ADP
ejpam-1375	187	52	predictors	predictor	NOUN
ejpam-1375	187	53	with	with	ADP
ejpam-1375	187	54	the	the	DET
ejpam-1375	187	55	hybridized	hybridize	VERB
ejpam-1375	187	56	ga	ga	PROPN
ejpam-1375	187	57	with	with	ADP
ejpam-1375	187	58	regularization	regularization	NOUN
ejpam-1375	187	59	of	of	ADP
ejpam-1375	187	60	the	the	DET
ejpam-1375	187	61	regression	regression	NOUN
ejpam-1375	187	62	trees	tree	NOUN
ejpam-1375	187	63	and	and	CCONJ
ejpam-1375	187	64	rbf	rbf	PROPN
ejpam-1375	187	65	networks	network	NOUN
ejpam-1375	187	66	.	.	PUNCT
ejpam-1375	188	1	the	the	DET
ejpam-1375	188	2	complexity	complexity	NOUN
ejpam-1375	188	3	of	of	ADP
ejpam-1375	188	4	a	a	DET
ejpam-1375	188	5	nonparametric	nonparametric	NOUN
ejpam-1375	188	6	regression	regression	NOUN
ejpam-1375	188	7	model	model	NOUN
ejpam-1375	188	8	increases	increase	VERB
ejpam-1375	188	9	with	with	ADP
ejpam-1375	188	10	the	the	DET
ejpam-1375	188	11	number	number	NOUN
ejpam-1375	188	12	of	of	ADP
ejpam-1375	188	13	independent	independent	ADJ
ejpam-1375	188	14	and	and	CCONJ
ejpam-1375	188	15	adjustable	adjustable	ADJ
ejpam-1375	188	16	parameters	parameter	NOUN
ejpam-1375	188	17	,	,	PUNCT
ejpam-1375	188	18	also	also	ADV
ejpam-1375	188	19	termed	term	VERB
ejpam-1375	188	20	effective	effective	ADJ
ejpam-1375	188	21	degrees	degree	NOUN
ejpam-1375	188	22	of	of	ADP
ejpam-1375	188	23	freedom	freedom	NOUN
ejpam-1375	188	24	,	,	PUNCT
ejpam-1375	188	25	in	in	ADP
ejpam-1375	188	26	the	the	DET
ejpam-1375	188	27	model	model	NOUN
ejpam-1375	188	28	.	.	PUNCT
ejpam-1375	189	1	according	accord	VERB
ejpam-1375	189	2	to	to	ADP
ejpam-1375	189	3	the	the	DET
ejpam-1375	189	4	qualitative	qualitative	ADJ
ejpam-1375	189	5	principle	principle	NOUN
ejpam-1375	189	6	of	of	ADP
ejpam-1375	189	7	occam	occam	PROPN
ejpam-1375	189	8	’s	’s	PART
ejpam-1375	189	9	razor	razor	NOUN
ejpam-1375	189	10	,	,	PUNCT
ejpam-1375	189	11	we	we	PRON
ejpam-1375	189	12	need	need	VERB
ejpam-1375	189	13	to	to	PART
ejpam-1375	189	14	find	find	VERB
ejpam-1375	189	15	the	the	DET
ejpam-1375	189	16	simplest	simple	ADJ
ejpam-1375	189	17	model	model	NOUN
ejpam-1375	189	18	that	that	PRON
ejpam-1375	189	19	fits	fit	VERB
ejpam-1375	189	20	the	the	DET
ejpam-1375	189	21	observed	observe	VERB
ejpam-1375	189	22	data	datum	NOUN
ejpam-1375	189	23	.	.	PUNCT
ejpam-1375	190	1	we	we	PRON
ejpam-1375	190	2	need	need	VERB
ejpam-1375	190	3	to	to	PART
ejpam-1375	190	4	provide	provide	VERB
ejpam-1375	190	5	a	a	DET
ejpam-1375	190	6	trade	trade	NOUN
ejpam-1375	190	7	off	off	ADP
ejpam-1375	190	8	between	between	ADP
ejpam-1375	190	9	how	how	SCONJ
ejpam-1375	190	10	well	well	ADV
ejpam-1375	190	11	the	the	DET
ejpam-1375	190	12	model	model	NOUN
ejpam-1375	190	13	fits	fit	VERB
ejpam-1375	190	14	the	the	DET
ejpam-1375	190	15	data	datum	NOUN
ejpam-1375	190	16	and	and	CCONJ
ejpam-1375	190	17	the	the	DET
ejpam-1375	190	18	model	model	NOUN
ejpam-1375	190	19	complexity	complexity	NOUN
ejpam-1375	190	20	.	.	PUNCT
ejpam-1375	191	1	o.	o.	PROPN
ejpam-1375	191	2	akbilgic	akbilgic	PROPN
ejpam-1375	191	3	,	,	PUNCT
ejpam-1375	191	4	h.	h.	PROPN
ejpam-1375	191	5	bozdogan	bozdogan	PROPN
ejpam-1375	191	6	/	/	SYM
ejpam-1375	191	7	eur	eur	PROPN
ejpam-1375	191	8	.	.	PUNCT
ejpam-1375	192	1	j.	j.	PROPN
ejpam-1375	192	2	pure	pure	PROPN
ejpam-1375	192	3	appl	appl	PROPN
ejpam-1375	192	4	.	.	PROPN
ejpam-1375	192	5	math	math	PROPN
ejpam-1375	192	6	,	,	PUNCT
ejpam-1375	192	7	4	4	NUM
ejpam-1375	192	8	(	(	PUNCT
ejpam-1375	192	9	2011	2011	NUM
ejpam-1375	192	10	)	)	PUNCT
ejpam-1375	192	11	,	,	PUNCT
ejpam-1375	192	12	467	467	NUM
ejpam-1375	192	13	-	-	SYM
ejpam-1375	192	14	485	485	NUM
ejpam-1375	192	15	475	475	NUM
ejpam-1375	192	16	the	the	DET
ejpam-1375	192	17	derived	derive	VERB
ejpam-1375	192	18	forms	form	NOUN
ejpam-1375	192	19	of	of	ADP
ejpam-1375	192	20	information	information	NOUN
ejpam-1375	192	21	criteria	criterion	NOUN
ejpam-1375	192	22	used	use	VERB
ejpam-1375	192	23	to	to	PART
ejpam-1375	192	24	evaluate	evaluate	VERB
ejpam-1375	192	25	and	and	CCONJ
ejpam-1375	192	26	compare	compare	VERB
ejpam-1375	192	27	different	different	ADJ
ejpam-1375	192	28	horizontal	horizontal	ADJ
ejpam-1375	192	29	and	and	CCONJ
ejpam-1375	192	30	vertical	vertical	ADJ
ejpam-1375	192	31	subset	subset	NOUN
ejpam-1375	192	32	selection	selection	NOUN
ejpam-1375	192	33	in	in	ADP
ejpam-1375	192	34	the	the	DET
ejpam-1375	192	35	genetic	genetic	ADJ
ejpam-1375	192	36	algorithm	algorithm	NOUN
ejpam-1375	192	37	(	(	PUNCT
ejpam-1375	192	38	ga	ga	PROPN
ejpam-1375	192	39	)	)	PUNCT
ejpam-1375	192	40	for	for	ADP
ejpam-1375	192	41	the	the	DET
ejpam-1375	192	42	regularized	regularize	VERB
ejpam-1375	192	43	regression	regression	NOUN
ejpam-1375	192	44	trees	tree	NOUN
ejpam-1375	192	45	and	and	CCONJ
ejpam-1375	192	46	rbf	rbf	PROPN
ejpam-1375	192	47	networks	network	NOUN
ejpam-1375	192	48	model	model	NOUN
ejpam-1375	192	49	given	give	VERB
ejpam-1375	192	50	by	by	ADP
ejpam-1375	192	51	(	(	PUNCT
ejpam-1375	192	52	2	2	NUM
ejpam-1375	192	53	)	)	PUNCT
ejpam-1375	192	54	under	under	ADP
ejpam-1375	192	55	the	the	DET
ejpam-1375	192	56	assumption	assumption	NOUN
ejpam-1375	192	57	:	:	PUNCT
ejpam-1375	192	58	ǫ	ǫ	PRON
ejpam-1375	192	59	∼	∼	NOUN
ejpam-1375	192	60	n	n	PRON
ejpam-1375	192	61	�	�	NOUN
ejpam-1375	192	62	0,σ2	0,σ2	NOUN
ejpam-1375	193	1	i	i	PRON
ejpam-1375	193	2	�	�	PROPN
ejpam-1375	193	3	or	or	CCONJ
ejpam-1375	193	4	equivalently	equivalently	ADV
ejpam-1375	193	5	ǫi	ǫi	AUX
ejpam-1375	193	6	∼	∼	NOUN
ejpam-1375	193	7	n	n	DET
ejpam-1375	193	8	�	�	PROPN
ejpam-1375	193	9	0,σ2	0,σ2	SYM
ejpam-1375	193	10	�	�	PROPN
ejpam-1375	193	11	or	or	CCONJ
ejpam-1375	193	12	i	i	NOUN
ejpam-1375	193	13	=	=	NOUN
ejpam-1375	193	14	1,2	1,2	NUM
ejpam-1375	193	15	,	,	PUNCT
ejpam-1375	193	16	.	.	PUNCT
ejpam-1375	193	17	.	.	PUNCT
ejpam-1375	194	1	.	.	PUNCT
ejpam-1375	195	1	,	,	PUNCT
ejpam-1375	195	2	n.	n.	PROPN
ejpam-1375	195	3	are	be	AUX
ejpam-1375	195	4	defined	define	VERB
ejpam-1375	195	5	as	as	ADP
ejpam-1375	195	6	follows	follow	VERB
ejpam-1375	195	7	.	.	PUNCT
ejpam-1375	196	1	1	1	X
ejpam-1375	196	2	.	.	X
ejpam-1375	196	3	several	several	ADJ
ejpam-1375	196	4	forms	form	NOUN
ejpam-1375	196	5	of	of	ADP
ejpam-1375	196	6	icomp	icomp	NOUN
ejpam-1375	196	7	based	base	VERB
ejpam-1375	196	8	on	on	ADP
ejpam-1375	196	9	information	information	NOUN
ejpam-1375	196	10	complexity	complexity	NOUN
ejpam-1375	196	11	measures	measure	NOUN
ejpam-1375	196	12	[	[	X
ejpam-1375	196	13	5	5	NUM
ejpam-1375	196	14	,	,	PUNCT
ejpam-1375	196	15	6	6	NUM
ejpam-1375	196	16	,	,	PUNCT
ejpam-1375	196	17	7	7	NUM
ejpam-1375	196	18	,	,	PUNCT
ejpam-1375	196	19	8	8	NUM
ejpam-1375	196	20	]	]	PUNCT
ejpam-1375	196	21	:	:	PUNCT
ejpam-1375	196	22	one	one	NUM
ejpam-1375	196	23	of	of	ADP
ejpam-1375	196	24	the	the	DET
ejpam-1375	196	25	general	general	ADJ
ejpam-1375	196	26	forms	form	NOUN
ejpam-1375	196	27	of	of	ADP
ejpam-1375	196	28	icomp	icomp	PROPN
ejpam-1375	196	29	is	be	AUX
ejpam-1375	196	30	an	an	DET
ejpam-1375	196	31	approximation	approximation	NOUN
ejpam-1375	196	32	to	to	ADP
ejpam-1375	196	33	the	the	DET
ejpam-1375	196	34	sum	sum	NOUN
ejpam-1375	196	35	of	of	ADP
ejpam-1375	196	36	two	two	NUM
ejpam-1375	196	37	kullback	kullback	NOUN
ejpam-1375	196	38	-	-	PUNCT
ejpam-1375	196	39	leibler	leibler	NOUN
ejpam-1375	196	40	(	(	PUNCT
ejpam-1375	196	41	kl	kl	PROPN
ejpam-1375	196	42	)	)	PUNCT
ejpam-1375	197	1	[	[	X
ejpam-1375	197	2	13	13	NUM
ejpam-1375	197	3	]	]	SYM
ejpam-1375	197	4	distances	distance	NOUN
ejpam-1375	197	5	.	.	PUNCT
ejpam-1375	197	6	•	•	NUM
ejpam-1375	197	7	for	for	ADP
ejpam-1375	197	8	general	general	ADJ
ejpam-1375	197	9	multivariate	multivariate	NOUN
ejpam-1375	197	10	normal	normal	ADJ
ejpam-1375	197	11	linear	linear	NOUN
ejpam-1375	197	12	or	or	CCONJ
ejpam-1375	197	13	nonlinear	nonlinear	ADJ
ejpam-1375	197	14	structural	structural	ADJ
ejpam-1375	197	15	models	model	NOUN
ejpam-1375	197	16	,	,	PUNCT
ejpam-1375	197	17	suppose	suppose	VERB
ejpam-1375	197	18	c1	c1	PROPN
ejpam-1375	197	19	�	�	PROPN
ejpam-1375	197	20	σ̂model	σ̂model	PROPN
ejpam-1375	197	21	�	�	PROPN
ejpam-1375	197	22	is	be	AUX
ejpam-1375	197	23	approximated	approximate	VERB
ejpam-1375	197	24	by	by	ADP
ejpam-1375	197	25	the	the	DET
ejpam-1375	197	26	complexity	complexity	NOUN
ejpam-1375	197	27	of	of	ADP
ejpam-1375	197	28	the	the	DET
ejpam-1375	197	29	inverse	inverse	NOUN
ejpam-1375	197	30	-	-	PUNCT
ejpam-1375	197	31	fisher	fisher	PROPN
ejpam-1375	197	32	information	information	NOUN
ejpam-1375	197	33	matrix	matrix	NOUN
ejpam-1375	197	34	(	(	PUNCT
ejpam-1375	197	35	ifim	ifim	NOUN
ejpam-1375	197	36	)	)	PUNCT
ejpam-1375	197	37	c1	c1	PROPN
ejpam-1375	197	38	�	�	PROPN
ejpam-1375	197	39	f̂−1	f̂−1	PROPN
ejpam-1375	197	40	�	�	PROPN
ejpam-1375	197	41	θ̂	θ̂	X
ejpam-1375	197	42	�	�	PROPN
ejpam-1375	197	43	�	�	PROPN
ejpam-1375	197	44	,	,	PUNCT
ejpam-1375	197	45	then	then	ADV
ejpam-1375	197	46	we	we	PRON
ejpam-1375	197	47	define	define	VERB
ejpam-1375	197	48	icomp(ifim	icomp(ifim	NOUN
ejpam-1375	197	49	)	)	PUNCT
ejpam-1375	197	50	as	as	ADP
ejpam-1375	197	51	icom	icom	PROPN
ejpam-1375	197	52	p(i	p(i	PROPN
ejpam-1375	197	53	f	f	PROPN
ejpam-1375	197	54	i	i	PRON
ejpam-1375	197	55	m	m	VERB
ejpam-1375	197	56	)	)	PUNCT
ejpam-1375	197	57	=	=	SYM
ejpam-1375	197	58	−2log	−2log	PROPN
ejpam-1375	197	59	l	l	PROPN
ejpam-1375	197	60	�	�	PROPN
ejpam-1375	197	61	θ̂	θ̂	PUNCT
ejpam-1375	197	62	�	�	PROPN
ejpam-1375	197	63	+	+	CCONJ
ejpam-1375	197	64	2c1	2c1	NUM
ejpam-1375	197	65	�	�	PROPN
ejpam-1375	197	66	f̂−1	f̂−1	PROPN
ejpam-1375	197	67	�	�	PROPN
ejpam-1375	197	68	θ̂	θ̂	X
ejpam-1375	197	69	�	�	PROPN
ejpam-1375	197	70	�	�	PROPN
ejpam-1375	197	71	(	(	PUNCT
ejpam-1375	197	72	29	29	NUM
ejpam-1375	197	73	)	)	PUNCT
ejpam-1375	197	74	c1	c1	NOUN
ejpam-1375	197	75	(	(	PUNCT
ejpam-1375	197	76	·	·	PUNCT
ejpam-1375	197	77	)	)	PUNCT
ejpam-1375	197	78	is	be	AUX
ejpam-1375	197	79	a	a	DET
ejpam-1375	197	80	maximal	maximal	ADJ
ejpam-1375	197	81	information	information	NOUN
ejpam-1375	197	82	theoretic	theoretic	NOUN
ejpam-1375	197	83	measure	measure	NOUN
ejpam-1375	197	84	of	of	ADP
ejpam-1375	197	85	complexity	complexity	NOUN
ejpam-1375	197	86	of	of	ADP
ejpam-1375	197	87	ifim	ifim	NOUN
ejpam-1375	197	88	of	of	ADP
ejpam-1375	197	89	a	a	DET
ejpam-1375	197	90	multivariate	multivariate	NOUN
ejpam-1375	197	91	normal	normal	ADJ
ejpam-1375	197	92	distribution	distribution	NOUN
ejpam-1375	197	93	given	give	VERB
ejpam-1375	197	94	by	by	ADP
ejpam-1375	197	95	c1	c1	PROPN
ejpam-1375	197	96	�	�	PROPN
ejpam-1375	197	97	f̂−1	f̂−1	PROPN
ejpam-1375	197	98	�	�	PROPN
ejpam-1375	197	99	θ̂	θ̂	X
ejpam-1375	197	100	�	�	PROPN
ejpam-1375	197	101	�	�	PROPN
ejpam-1375	197	102	=	=	SYM
ejpam-1375	197	103	s	s	PART
ejpam-1375	197	104	2	2	NUM
ejpam-1375	197	105	log	log	NOUN
ejpam-1375	197	106	l	l	NOUN
ejpam-1375	197	107	t	t	NOUN
ejpam-1375	197	108	r	r	NOUN
ejpam-1375	197	109	�	�	PROPN
ejpam-1375	197	110	f̂−1	f̂−1	PROPN
ejpam-1375	197	111	�	�	PROPN
ejpam-1375	197	112	θ̂	θ̂	X
ejpam-1375	197	113	�	�	PROPN
ejpam-1375	197	114	�	�	PROPN
ejpam-1375	197	115	s	s	PART
ejpam-1375	197	116	!	!	PUNCT
ejpam-1375	198	1	−	−	NOUN
ejpam-1375	198	2	1	1	NUM
ejpam-1375	198	3	2	2	NUM
ejpam-1375	198	4	log	log	NOUN
ejpam-1375	198	5	|	|	ADV
ejpam-1375	198	6	f̂−1	f̂−1	PROPN
ejpam-1375	198	7	�	�	PROPN
ejpam-1375	198	8	θ̂	θ̂	PUNCT
ejpam-1375	198	9	�	�	PROPN
ejpam-1375	198	10	|	|	ADV
ejpam-1375	198	11	(	(	PUNCT
ejpam-1375	198	12	30	30	NUM
ejpam-1375	198	13	)	)	PUNCT
ejpam-1375	198	14	where	where	SCONJ
ejpam-1375	198	15	s	s	NOUN
ejpam-1375	198	16	=	=	SYM
ejpam-1375	198	17	dim	dim	ADJ
ejpam-1375	198	18	�	�	PROPN
ejpam-1375	198	19	f̂−1	f̂−1	PROPN
ejpam-1375	198	20	�	�	PROPN
ejpam-1375	198	21	=	=	PUNCT
ejpam-1375	198	22	rank	rank	PROPN
ejpam-1375	198	23	�	�	PROPN
ejpam-1375	198	24	f̂−1	f̂−1	PROPN
ejpam-1375	198	25	�	�	PROPN
ejpam-1375	198	26	.	.	PUNCT
ejpam-1375	199	1	for	for	ADP
ejpam-1375	199	2	the	the	DET
ejpam-1375	199	3	regression	regression	NOUN
ejpam-1375	199	4	trees	tree	NOUN
ejpam-1375	199	5	and	and	CCONJ
ejpam-1375	199	6	rbf	rbf	PROPN
ejpam-1375	199	7	networks	network	NOUN
ejpam-1375	199	8	,	,	PUNCT
ejpam-1375	199	9	the	the	DET
ejpam-1375	199	10	estimated	estimate	VERB
ejpam-1375	199	11	inverse	inverse	NOUN
ejpam-1375	199	12	fisher	fisher	PROPN
ejpam-1375	199	13	information	information	NOUN
ejpam-1375	199	14	matrix	matrix	NOUN
ejpam-1375	199	15	(	(	PUNCT
ejpam-1375	199	16	ifim	ifim	NOUN
ejpam-1375	199	17	)	)	PUNCT
ejpam-1375	199	18	is	be	AUX
ejpam-1375	199	19	given	give	VERB
ejpam-1375	199	20	by	by	ADP
ejpam-1375	199	21	ôcov	ôcov	PROPN
ejpam-1375	199	22	�	�	PROPN
ejpam-1375	199	23	ŵ	ŵ	PROPN
ejpam-1375	199	24	,	,	PUNCT
ejpam-1375	199	25	σ̂2	σ̂2	PROPN
ejpam-1375	199	26	�	�	PROPN
ejpam-1375	199	27	=	=	SYM
ejpam-1375	200	1	f̂−1	f̂−1	PROPN
ejpam-1375	200	2	=	=	SYM
ejpam-1375	200	3			PROPN
ejpam-1375	200	4			NOUN
ejpam-1375	200	5	σ̂	σ̂	NUM
ejpam-1375	200	6	2	2	NUM
ejpam-1375	200	7	�	�	PROPN
ejpam-1375	200	8	h	h	NOUN
ejpam-1375	200	9	′h	′h	PROPN
ejpam-1375	200	10	�	�	VERB
ejpam-1375	200	11	−1	−1	NOUN
ejpam-1375	200	12	0	0	NUM
ejpam-1375	200	13	0	0	NUM
ejpam-1375	200	14	2σ̂4	2σ̂4	NUM
ejpam-1375	200	15	4	4	NUM
ejpam-1375	200	16			PROPN
ejpam-1375	200	17			PROPN
ejpam-1375	200	18	,	,	PUNCT
ejpam-1375	200	19	(	(	PUNCT
ejpam-1375	200	20	31	31	NUM
ejpam-1375	200	21	)	)	PUNCT
ejpam-1375	200	22	where	where	SCONJ
ejpam-1375	200	23	σ̂2	σ̂2	PROPN
ejpam-1375	200	24	=	=	SYM
ejpam-1375	200	25	�	�	PROPN
ejpam-1375	200	26	y	y	PROPN
ejpam-1375	200	27	−h	−h	VERB
ejpam-1375	201	1	bw	bw	PROPN
ejpam-1375	201	2	�	�	PROPN
ejpam-1375	201	3	′	′	NUM
ejpam-1375	201	4	�	�	PROPN
ejpam-1375	201	5	y	y	PROPN
ejpam-1375	201	6	−h	−h	PROPN
ejpam-1375	201	7	bw	bw	PROPN
ejpam-1375	201	8	�	�	PROPN
ejpam-1375	201	9	n	n	PROPN
ejpam-1375	201	10	.	.	PUNCT
ejpam-1375	202	1	(	(	PUNCT
ejpam-1375	202	2	32	32	NUM
ejpam-1375	202	3	)	)	PUNCT
ejpam-1375	202	4	then	then	ADV
ejpam-1375	202	5	,	,	PUNCT
ejpam-1375	202	6	icomp(ifim	icomp(ifim	NOUN
ejpam-1375	202	7	)	)	PUNCT
ejpam-1375	202	8	using	use	VERB
ejpam-1375	202	9	the	the	DET
ejpam-1375	202	10	definition	definition	NOUN
ejpam-1375	202	11	,	,	PUNCT
ejpam-1375	202	12	becomes	become	VERB
ejpam-1375	202	13	:	:	PUNCT
ejpam-1375	202	14	icom	icom	PROPN
ejpam-1375	202	15	p(i	p(i	PROPN
ejpam-1375	202	16	f	f	PROPN
ejpam-1375	202	17	i	i	PRON
ejpam-1375	202	18	m	m	VERB
ejpam-1375	202	19	)	)	PUNCT
ejpam-1375	203	1	=	=	SYM
ejpam-1375	203	2	nln	nln	NOUN
ejpam-1375	203	3	(	(	PUNCT
ejpam-1375	203	4	2π)+	2π)+	NUM
ejpam-1375	203	5	nlog	nlog	PROPN
ejpam-1375	203	6	l	l	PROPN
ejpam-1375	203	7	�	�	PROPN
ejpam-1375	203	8	σ̂2	σ̂2	PROPN
ejpam-1375	203	9	�	�	PROPN
ejpam-1375	203	10	+	+	CCONJ
ejpam-1375	203	11	n+	n+	NUM
ejpam-1375	203	12	2c1	2c1	NUM
ejpam-1375	203	13	�	�	PROPN
ejpam-1375	203	14	f̂−1	f̂−1	PROPN
ejpam-1375	203	15	�	�	PROPN
ejpam-1375	203	16	θ̂	θ̂	X
ejpam-1375	203	17	�	�	PROPN
ejpam-1375	203	18	�	�	PROPN
ejpam-1375	203	19	(	(	PUNCT
ejpam-1375	203	20	33	33	NUM
ejpam-1375	203	21	)	)	PUNCT
ejpam-1375	203	22	where	where	SCONJ
ejpam-1375	203	23	the	the	DET
ejpam-1375	203	24	entropic	entropic	ADJ
ejpam-1375	203	25	complexity	complexity	NOUN
ejpam-1375	203	26	c1	c1	PROPN
ejpam-1375	203	27	�	�	PROPN
ejpam-1375	203	28	f̂−1	f̂−1	PROPN
ejpam-1375	203	29	�	�	PROPN
ejpam-1375	203	30	θ̂m	θ̂m	NOUN
ejpam-1375	203	31	�	�	PROPN
ejpam-1375	203	32	�	�	X
ejpam-1375	203	33	=	=	SYM
ejpam-1375	203	34	(	(	PUNCT
ejpam-1375	203	35	m+	m+	NOUN
ejpam-1375	203	36	1	1	NUM
ejpam-1375	203	37	)	)	PUNCT
ejpam-1375	203	38	log	log	PROPN
ejpam-1375	203	39			PROPN
ejpam-1375	203	40			VERB
ejpam-1375	203	41	t	t	PROPN
ejpam-1375	203	42	rσ̂2	rσ̂2	PROPN
ejpam-1375	203	43	�	�	PROPN
ejpam-1375	203	44	h	h	NOUN
ejpam-1375	203	45	′h	′h	PROPN
ejpam-1375	203	46	�	�	PROPN
ejpam-1375	203	47	−1	−1	ADV
ejpam-1375	203	48	+	+	CCONJ
ejpam-1375	203	49	2θ̂	2θ̂	NUM
ejpam-1375	203	50	4	4	NUM
ejpam-1375	203	51	4	4	NUM
ejpam-1375	203	52	m+	m+	NUM
ejpam-1375	203	53	1	1	NUM
ejpam-1375	203	54			PROPN
ejpam-1375	203	55			SYM
ejpam-1375	203	56	(	(	PUNCT
ejpam-1375	203	57	34	34	NUM
ejpam-1375	203	58	)	)	PUNCT
ejpam-1375	203	59	−	−	NOUN
ejpam-1375	203	60	1	1	NUM
ejpam-1375	203	61	2	2	NUM
ejpam-1375	203	62	log	log	NOUN
ejpam-1375	204	1	|	|	INTJ
ejpam-1375	204	2	σ̂2	σ̂2	PROPN
ejpam-1375	204	3	�	�	PROPN
ejpam-1375	204	4	h	h	PROPN
ejpam-1375	204	5	′h	′h	PROPN
ejpam-1375	204	6	�	�	PROPN
ejpam-1375	204	7	−1	−1	NOUN
ejpam-1375	204	8	|+log	|+log	PROPN
ejpam-1375	204	9	�	�	PROPN
ejpam-1375	204	10	2σ̂4	2σ̂4	NUM
ejpam-1375	204	11	4	4	NUM
ejpam-1375	204	12	�	�	NOUN
ejpam-1375	204	13	we	we	PRON
ejpam-1375	204	14	can	can	AUX
ejpam-1375	204	15	also	also	ADV
ejpam-1375	204	16	define	define	VERB
ejpam-1375	204	17	icomp	icomp	NOUN
ejpam-1375	204	18	for	for	ADP
ejpam-1375	204	19	misspecified	misspecified	ADJ
ejpam-1375	204	20	models	model	NOUN
ejpam-1375	204	21	.	.	PUNCT
ejpam-1375	205	1	o.	o.	PROPN
ejpam-1375	205	2	akbilgic	akbilgic	PROPN
ejpam-1375	205	3	,	,	PUNCT
ejpam-1375	205	4	h.	h.	PROPN
ejpam-1375	205	5	bozdogan	bozdogan	PROPN
ejpam-1375	205	6	/	/	SYM
ejpam-1375	205	7	eur	eur	PROPN
ejpam-1375	205	8	.	.	PUNCT
ejpam-1375	206	1	j.	j.	PROPN
ejpam-1375	206	2	pure	pure	PROPN
ejpam-1375	206	3	appl	appl	PROPN
ejpam-1375	206	4	.	.	PROPN
ejpam-1375	206	5	math	math	PROPN
ejpam-1375	206	6	,	,	PUNCT
ejpam-1375	206	7	4	4	NUM
ejpam-1375	206	8	(	(	PUNCT
ejpam-1375	206	9	2011	2011	NUM
ejpam-1375	206	10	)	)	PUNCT
ejpam-1375	206	11	,	,	PUNCT
ejpam-1375	206	12	467	467	NUM
ejpam-1375	206	13	-	-	SYM
ejpam-1375	206	14	485	485	NUM
ejpam-1375	206	15	476	476	NUM
ejpam-1375	206	16	•	•	NOUN
ejpam-1375	206	17	icomp	icomp	ADJ
ejpam-1375	206	18	under	under	ADP
ejpam-1375	206	19	misspecification	misspecification	NOUN
ejpam-1375	206	20	:	:	PUNCT
ejpam-1375	207	1	icom	icom	PROPN
ejpam-1375	207	2	p(i	p(i	PROPN
ejpam-1375	207	3	f	f	PROPN
ejpam-1375	207	4	im)misspec	im)misspec	PROPN
ejpam-1375	207	5	=	=	PUNCT
ejpam-1375	207	6	−2lnl	−2lnl	PROPN
ejpam-1375	207	7	�	�	PROPN
ejpam-1375	207	8	θ̂	θ̂	PUNCT
ejpam-1375	207	9	�	�	PROPN
ejpam-1375	207	10	+	+	CCONJ
ejpam-1375	207	11	2c1	2c1	NUM
ejpam-1375	207	12	�	�	NOUN
ejpam-1375	207	13	ôcov	ôcov	VERB
ejpam-1375	207	14	�	�	PROPN
ejpam-1375	207	15	θ̂	θ̂	PUNCT
ejpam-1375	207	16	�	�	PROPN
ejpam-1375	207	17	misspec	misspec	PROPN
ejpam-1375	207	18	�	�	PROPN
ejpam-1375	207	19	(	(	PUNCT
ejpam-1375	207	20	35	35	NUM
ejpam-1375	207	21	)	)	PUNCT
ejpam-1375	207	22	=	=	SYM
ejpam-1375	207	23	nln	nln	NOUN
ejpam-1375	207	24	(	(	PUNCT
ejpam-1375	207	25	2π	2π	NOUN
ejpam-1375	207	26	)	)	PUNCT
ejpam-1375	208	1	+	+	CCONJ
ejpam-1375	208	2	nln	nln	PROPN
ejpam-1375	208	3	�	�	PROPN
ejpam-1375	208	4	σ̂2	σ̂2	PROPN
ejpam-1375	208	5	�	�	PROPN
ejpam-1375	208	6	+	+	CCONJ
ejpam-1375	208	7	n+	n+	NUM
ejpam-1375	208	8	2c1	2c1	NUM
ejpam-1375	208	9	�	�	PROPN
ejpam-1375	208	10	ôcov	ôcov	VERB
ejpam-1375	208	11	�	�	PROPN
ejpam-1375	208	12	θ̂	θ̂	PUNCT
ejpam-1375	208	13	�	�	PROPN
ejpam-1375	208	14	misspec	misspec	PROPN
ejpam-1375	208	15	�	�	PROPN
ejpam-1375	208	16	where	where	SCONJ
ejpam-1375	208	17	ôcov	ôcov	PROPN
ejpam-1375	208	18	�	�	PROPN
ejpam-1375	208	19	θ̂	θ̂	PUNCT
ejpam-1375	208	20	�	�	PROPN
ejpam-1375	208	21	misspec	misspec	NOUN
ejpam-1375	208	22	=	=	PUNCT
ejpam-1375	208	23	f̂−1brf̂−1	f̂−1brf̂−1	X
ejpam-1375	208	24	(	(	PUNCT
ejpam-1375	208	25	36	36	NUM
ejpam-1375	208	26	)	)	PUNCT
ejpam-1375	208	27	is	be	AUX
ejpam-1375	208	28	a	a	DET
ejpam-1375	208	29	consistent	consistent	ADJ
ejpam-1375	208	30	estimator	estimator	NOUN
ejpam-1375	208	31	of	of	ADP
ejpam-1375	208	32	the	the	DET
ejpam-1375	208	33	covariance	covariance	NOUN
ejpam-1375	208	34	matrix	matrix	NOUN
ejpam-1375	208	35	cov	cov	PROPN
ejpam-1375	208	36	�	�	PROPN
ejpam-1375	208	37	θ	θ	PROPN
ejpam-1375	208	38	∗	∗	X
ejpam-1375	208	39	k	k	PROPN
ejpam-1375	208	40	�	�	PROPN
ejpam-1375	208	41	for	for	ADP
ejpam-1375	208	42	f̂−1	f̂−1	PROPN
ejpam-1375	208	43	=	=	PUNCT
ejpam-1375	208	44			PROPN
ejpam-1375	208	45			NOUN
ejpam-1375	208	46	σ̂	σ̂	NUM
ejpam-1375	208	47	2	2	NUM
ejpam-1375	208	48	�	�	PROPN
ejpam-1375	208	49	h	h	NOUN
ejpam-1375	208	50	′h	′h	PROPN
ejpam-1375	208	51	�	�	VERB
ejpam-1375	208	52	−1	−1	NOUN
ejpam-1375	208	53	0	0	NUM
ejpam-1375	208	54	0	0	NUM
ejpam-1375	209	1	2σ̂4	2σ̂4	NUM
ejpam-1375	209	2	4	4	NUM
ejpam-1375	209	3			PROPN
ejpam-1375	209	4			PROPN
ejpam-1375	209	5	,	,	PUNCT
ejpam-1375	209	6	and	and	CCONJ
ejpam-1375	209	7	r̂=	r̂=	ADJ
ejpam-1375	209	8			NOUN
ejpam-1375	209	9			NOUN
ejpam-1375	209	10	1	1	NUM
ejpam-1375	209	11	σ̂4	σ̂4	NOUN
ejpam-1375	209	12	h	h	NOUN
ejpam-1375	209	13	′d2h	′d2h	PROPN
ejpam-1375	209	14	h	h	PROPN
ejpam-1375	209	15	′1	′1	NOUN
ejpam-1375	209	16	sk	sk	PROPN
ejpam-1375	209	17	2σ̂3	2σ̂3	NUM
ejpam-1375	209	18	�	�	PROPN
ejpam-1375	209	19	h	h	PROPN
ejpam-1375	209	20	′1	′1	NOUN
ejpam-1375	209	21	sk	sk	PROPN
ejpam-1375	209	22	2σ̂3	2σ̂3	NUM
ejpam-1375	209	23	�	�	PROPN
ejpam-1375	209	24	′	′	NUM
ejpam-1375	209	25	(	(	PUNCT
ejpam-1375	209	26	n−m)(k	n−m)(k	PROPN
ejpam-1375	209	27	t−1	t−1	PROPN
ejpam-1375	209	28	)	)	PUNCT
ejpam-1375	209	29	4σ̂4	4σ̂4	NUM
ejpam-1375	209	30			PROPN
ejpam-1375	209	31			PROPN
ejpam-1375	209	32	.	.	PUNCT
ejpam-1375	210	1	this	this	PRON
ejpam-1375	210	2	is	be	AUX
ejpam-1375	210	3	often	often	ADV
ejpam-1375	210	4	called	call	VERB
ejpam-1375	210	5	the	the	DET
ejpam-1375	210	6	“	"	PUNCT
ejpam-1375	210	7	sandwich	sandwich	NOUN
ejpam-1375	210	8	covariance	covariance	NOUN
ejpam-1375	210	9	”	"	PUNCT
ejpam-1375	210	10	or	or	CCONJ
ejpam-1375	210	11	“	"	PUNCT
ejpam-1375	210	12	robust	robust	ADJ
ejpam-1375	210	13	covariance	covariance	NOUN
ejpam-1375	210	14	”	"	PUNCT
ejpam-1375	210	15	estimator	estimator	NOUN
ejpam-1375	210	16	,	,	PUNCT
ejpam-1375	210	17	since	since	SCONJ
ejpam-1375	210	18	it	it	PRON
ejpam-1375	210	19	is	be	AUX
ejpam-1375	210	20	a	a	DET
ejpam-1375	210	21	correct	correct	ADJ
ejpam-1375	210	22	variance	variance	NOUN
ejpam-1375	210	23	regardless	regardless	ADV
ejpam-1375	210	24	whether	whether	SCONJ
ejpam-1375	210	25	of	of	ADP
ejpam-1375	210	26	the	the	DET
ejpam-1375	210	27	assumed	assumed	ADJ
ejpam-1375	210	28	model	model	NOUN
ejpam-1375	210	29	is	be	AUX
ejpam-1375	210	30	correct	correct	ADJ
ejpam-1375	210	31	or	or	CCONJ
ejpam-1375	210	32	not	not	PART
ejpam-1375	210	33	.	.	PUNCT
ejpam-1375	211	1	when	when	SCONJ
ejpam-1375	211	2	the	the	DET
ejpam-1375	211	3	model	model	NOUN
ejpam-1375	211	4	is	be	AUX
ejpam-1375	211	5	correct	correct	ADJ
ejpam-1375	211	6	we	we	PRON
ejpam-1375	211	7	get	get	VERB
ejpam-1375	211	8	bf	bf	NOUN
ejpam-1375	211	9	=	=	SYM
ejpam-1375	211	10	br	br	NOUN
ejpam-1375	211	11	,	,	PUNCT
ejpam-1375	211	12	and	and	CCONJ
ejpam-1375	211	13	the	the	DET
ejpam-1375	211	14	formula	formula	NOUN
ejpam-1375	211	15	reduces	reduce	VERB
ejpam-1375	211	16	to	to	ADP
ejpam-1375	211	17	the	the	DET
ejpam-1375	211	18	usual	usual	ADJ
ejpam-1375	211	19	inverse	inverse	NOUN
ejpam-1375	211	20	fisher	fisher	PROPN
ejpam-1375	211	21	information	information	NOUN
ejpam-1375	211	22	matrix	matrix	NOUN
ejpam-1375	212	1	bf−1	bf−1	PROPN
ejpam-1375	212	2	[	[	X
ejpam-1375	212	3	24	24	NUM
ejpam-1375	212	4	]	]	PUNCT
ejpam-1375	212	5	.	.	PUNCT
ejpam-1375	213	1	note	note	VERB
ejpam-1375	213	2	that	that	SCONJ
ejpam-1375	213	3	this	this	DET
ejpam-1375	213	4	covariance	covariance	NOUN
ejpam-1375	213	5	matrix	matrix	NOUN
ejpam-1375	213	6	takes	take	VERB
ejpam-1375	213	7	into	into	ADP
ejpam-1375	213	8	account	account	NOUN
ejpam-1375	213	9	presence	presence	NOUN
ejpam-1375	213	10	of	of	ADP
ejpam-1375	213	11	skewness	skewness	NOUN
ejpam-1375	213	12	and	and	CCONJ
ejpam-1375	213	13	kurtosis	kurtosis	NOUN
ejpam-1375	213	14	which	which	PRON
ejpam-1375	213	15	is	be	AUX
ejpam-1375	213	16	not	not	PART
ejpam-1375	213	17	possible	possible	ADJ
ejpam-1375	213	18	with	with	ADP
ejpam-1375	213	19	aic	aic	PROPN
ejpam-1375	213	20	,	,	PUNCT
ejpam-1375	213	21	and	and	CCONJ
ejpam-1375	213	22	mdl	mdl	PROPN
ejpam-1375	213	23	/	/	SYM
ejpam-1375	213	24	sbc	sbc	PROPN
ejpam-1375	213	25	.	.	PROPN
ejpam-1375	214	1	2	2	X
ejpam-1375	214	2	.	.	X
ejpam-1375	214	3	akaike	akaike	PROPN
ejpam-1375	214	4	’s	’s	PART
ejpam-1375	214	5	information	information	NOUN
ejpam-1375	214	6	criterion	criterion	NOUN
ejpam-1375	214	7	(	(	PUNCT
ejpam-1375	214	8	aic	aic	PROPN
ejpam-1375	214	9	)	)	PUNCT
ejpam-1375	215	1	[	[	X
ejpam-1375	215	2	1	1	NUM
ejpam-1375	215	3	]	]	SYM
ejpam-1375	215	4	:	:	PUNCT
ejpam-1375	215	5	aic(m	aic(m	X
ejpam-1375	215	6	)	)	PUNCT
ejpam-1375	216	1	=	=	SYM
ejpam-1375	216	2	nln	nln	NOUN
ejpam-1375	216	3	(	(	PUNCT
ejpam-1375	216	4	2π	2π	NOUN
ejpam-1375	216	5	)	)	PUNCT
ejpam-1375	217	1	+	+	CCONJ
ejpam-1375	217	2	nln	nln	PROPN
ejpam-1375	217	3	�	�	PROPN
ejpam-1375	217	4	�	�	PROPN
ejpam-1375	217	5	y	y	PROPN
ejpam-1375	217	6	−h	−h	VERB
ejpam-1375	217	7	bw	bw	PROPN
ejpam-1375	217	8	�	�	PROPN
ejpam-1375	217	9	′	′	NUM
ejpam-1375	217	10	�	�	PROPN
ejpam-1375	217	11	y	y	PROPN
ejpam-1375	217	12	−h	−h	PROPN
ejpam-1375	217	13	bw	bw	PROPN
ejpam-1375	217	14	�	�	PROPN
ejpam-1375	217	15	n	n	CCONJ
ejpam-1375	217	16	�	�	PROPN
ejpam-1375	217	17	+	+	CCONJ
ejpam-1375	217	18	n+	n+	NUM
ejpam-1375	217	19	2	2	NUM
ejpam-1375	217	20	(	(	PUNCT
ejpam-1375	217	21	m+	m+	NOUN
ejpam-1375	217	22	1	1	NUM
ejpam-1375	217	23	)	)	PUNCT
ejpam-1375	217	24	(	(	PUNCT
ejpam-1375	217	25	37	37	NUM
ejpam-1375	217	26	)	)	PUNCT
ejpam-1375	217	27	3	3	NUM
ejpam-1375	217	28	.	.	PUNCT
ejpam-1375	217	29	schwartz	schwartz	PROPN
ejpam-1375	217	30	bayesian	bayesian	PROPN
ejpam-1375	217	31	(	(	PUNCT
ejpam-1375	217	32	sbc	sbc	NOUN
ejpam-1375	217	33	)	)	PUNCT
ejpam-1375	217	34	criterion	criterion	NOUN
ejpam-1375	217	35	[	[	X
ejpam-1375	217	36	21	21	NUM
ejpam-1375	217	37	]	]	X
ejpam-1375	217	38	:	:	PUNCT
ejpam-1375	217	39	sbc(m	sbc(m	X
ejpam-1375	217	40	)	)	PUNCT
ejpam-1375	217	41	=	=	SYM
ejpam-1375	217	42	nln	nln	NOUN
ejpam-1375	217	43	(	(	PUNCT
ejpam-1375	217	44	2π)+	2π)+	NUM
ejpam-1375	217	45	nln	nln	PROPN
ejpam-1375	217	46	�	�	PROPN
ejpam-1375	217	47	�	�	PROPN
ejpam-1375	217	48	y	y	PROPN
ejpam-1375	217	49	−h	−h	VERB
ejpam-1375	217	50	bw	bw	PROPN
ejpam-1375	217	51	�	�	PROPN
ejpam-1375	217	52	′	′	NUM
ejpam-1375	217	53	�	�	PROPN
ejpam-1375	217	54	y	y	PROPN
ejpam-1375	217	55	−h	−h	PROPN
ejpam-1375	217	56	bw	bw	PROPN
ejpam-1375	217	57	�	�	PROPN
ejpam-1375	217	58	n	n	CCONJ
ejpam-1375	217	59	�	�	PROPN
ejpam-1375	217	60	+	+	CCONJ
ejpam-1375	217	61	n+mlog	n+mlog	PROPN
ejpam-1375	217	62	(	(	PUNCT
ejpam-1375	217	63	n	n	CCONJ
ejpam-1375	217	64	)	)	PUNCT
ejpam-1375	217	65	(	(	PUNCT
ejpam-1375	217	66	38	38	NUM
ejpam-1375	217	67	)	)	PUNCT
ejpam-1375	217	68	4	4	NUM
ejpam-1375	217	69	.	.	PUNCT
ejpam-1375	217	70	consistent	consistent	ADJ
ejpam-1375	217	71	akaike	akaike	ADP
ejpam-1375	217	72	’s	’s	PART
ejpam-1375	217	73	information	information	NOUN
ejpam-1375	217	74	criterion	criterion	NOUN
ejpam-1375	217	75	using	use	VERB
ejpam-1375	217	76	fisher	fisher	PROPN
ejpam-1375	217	77	information	information	NOUN
ejpam-1375	217	78	(	(	PUNCT
ejpam-1375	217	79	caicf	caicf	NOUN
ejpam-1375	217	80	)	)	PUNCT
ejpam-1375	218	1	[	[	X
ejpam-1375	218	2	4	4	NUM
ejpam-1375	218	3	]	]	PUNCT
ejpam-1375	218	4	:	:	PUNCT
ejpam-1375	218	5	caic	caic	PROPN
ejpam-1375	218	6	f(m	f(m	PROPN
ejpam-1375	218	7	)	)	PUNCT
ejpam-1375	218	8	=	=	SYM
ejpam-1375	218	9	nln	nln	NOUN
ejpam-1375	218	10	(	(	PUNCT
ejpam-1375	218	11	2π)+	2π)+	NUM
ejpam-1375	218	12	nln	nln	PROPN
ejpam-1375	218	13	�	�	PROPN
ejpam-1375	218	14	�	�	PROPN
ejpam-1375	218	15	y	y	PROPN
ejpam-1375	218	16	−h	−h	VERB
ejpam-1375	218	17	bw	bw	PROPN
ejpam-1375	218	18	�	�	PROPN
ejpam-1375	218	19	′	′	NUM
ejpam-1375	218	20	�	�	PROPN
ejpam-1375	218	21	y	y	PROPN
ejpam-1375	218	22	−h	−h	PROPN
ejpam-1375	218	23	bw	bw	PROPN
ejpam-1375	218	24	�	�	PROPN
ejpam-1375	218	25	n	n	CCONJ
ejpam-1375	218	26	�	�	PROPN
ejpam-1375	218	27	+	+	CCONJ
ejpam-1375	218	28	n	n	CCONJ
ejpam-1375	218	29	(	(	PUNCT
ejpam-1375	218	30	39	39	NUM
ejpam-1375	218	31	)	)	PUNCT
ejpam-1375	218	32	+	+	CCONJ
ejpam-1375	218	33	2	2	NUM
ejpam-1375	218	34	(	(	PUNCT
ejpam-1375	218	35	m+	m+	NUM
ejpam-1375	218	36	1)+	1)+	NUM
ejpam-1375	218	37	log	log	NOUN
ejpam-1375	219	1	|	|	INTJ
ejpam-1375	219	2	f	f	PROPN
ejpam-1375	219	3	�	�	PROPN
ejpam-1375	219	4	θ̂k	θ̂k	NOUN
ejpam-1375	219	5	�	�	PROPN
ejpam-1375	219	6	|	|	ADV
ejpam-1375	219	7	where	where	SCONJ
ejpam-1375	219	8	f	f	PROPN
ejpam-1375	219	9	�	�	PROPN
ejpam-1375	219	10	θ̂k	θ̂k	NOUN
ejpam-1375	219	11	�	�	PROPN
ejpam-1375	219	12	is	be	AUX
ejpam-1375	219	13	the	the	DET
ejpam-1375	219	14	fisher	fisher	PROPN
ejpam-1375	219	15	information	information	NOUN
ejpam-1375	219	16	matrix	matrix	NOUN
ejpam-1375	219	17	at	at	ADP
ejpam-1375	219	18	the	the	DET
ejpam-1375	219	19	parameter	parameter	NOUN
ejpam-1375	219	20	estimation	estimation	PROPN
ejpam-1375	219	21	θ̂k	θ̂k	PROPN
ejpam-1375	219	22	.	.	PUNCT
ejpam-1375	219	23	o.	o.	PROPN
ejpam-1375	219	24	akbilgic	akbilgic	PROPN
ejpam-1375	219	25	,	,	PUNCT
ejpam-1375	219	26	h.	h.	PROPN
ejpam-1375	219	27	bozdogan	bozdogan	PROPN
ejpam-1375	219	28	/	/	SYM
ejpam-1375	219	29	eur	eur	PROPN
ejpam-1375	219	30	.	.	PUNCT
ejpam-1375	220	1	j.	j.	PROPN
ejpam-1375	220	2	pure	pure	PROPN
ejpam-1375	220	3	appl	appl	PROPN
ejpam-1375	220	4	.	.	PROPN
ejpam-1375	220	5	math	math	PROPN
ejpam-1375	220	6	,	,	PUNCT
ejpam-1375	220	7	4	4	NUM
ejpam-1375	220	8	(	(	PUNCT
ejpam-1375	220	9	2011	2011	NUM
ejpam-1375	220	10	)	)	PUNCT
ejpam-1375	220	11	,	,	PUNCT
ejpam-1375	220	12	467	467	NUM
ejpam-1375	220	13	-	-	SYM
ejpam-1375	220	14	485	485	NUM
ejpam-1375	220	15	477	477	NUM
ejpam-1375	220	16	7	7	NUM
ejpam-1375	220	17	.	.	PUNCT
ejpam-1375	221	1	genetic	genetic	ADJ
ejpam-1375	221	2	algorithm	algorithm	NOUN
ejpam-1375	221	3	for	for	ADP
ejpam-1375	221	4	subset	subset	ADJ
ejpam-1375	221	5	selection	selection	NOUN
ejpam-1375	221	6	the	the	DET
ejpam-1375	221	7	genetic	genetic	ADJ
ejpam-1375	221	8	algorithm	algorithm	NOUN
ejpam-1375	221	9	(	(	PUNCT
ejpam-1375	221	10	ga	ga	NOUN
ejpam-1375	221	11	)	)	PUNCT
ejpam-1375	221	12	is	be	AUX
ejpam-1375	221	13	a	a	DET
ejpam-1375	221	14	stochastic	stochastic	ADJ
ejpam-1375	221	15	or	or	CCONJ
ejpam-1375	221	16	probabilistic	probabilistic	ADJ
ejpam-1375	221	17	search	search	NOUN
ejpam-1375	221	18	algorithm	algorithm	NOUN
ejpam-1375	221	19	that	that	PRON
ejpam-1375	221	20	employs	employ	VERB
ejpam-1375	221	21	natural	natural	ADJ
ejpam-1375	221	22	selection	selection	NOUN
ejpam-1375	221	23	and	and	CCONJ
ejpam-1375	221	24	genetic	genetic	ADJ
ejpam-1375	221	25	operators	operator	NOUN
ejpam-1375	221	26	.	.	PUNCT
ejpam-1375	222	1	a	a	DET
ejpam-1375	222	2	ga	ga	PROPN
ejpam-1375	222	3	treats	treat	VERB
ejpam-1375	222	4	information	information	NOUN
ejpam-1375	222	5	as	as	ADP
ejpam-1375	222	6	a	a	DET
ejpam-1375	222	7	series	series	NOUN
ejpam-1375	222	8	of	of	ADP
ejpam-1375	222	9	codes	code	NOUN
ejpam-1375	222	10	on	on	ADP
ejpam-1375	222	11	a	a	DET
ejpam-1375	222	12	binary	binary	ADJ
ejpam-1375	222	13	string	string	NOUN
ejpam-1375	222	14	,	,	PUNCT
ejpam-1375	222	15	where	where	SCONJ
ejpam-1375	222	16	each	each	DET
ejpam-1375	222	17	string	string	NOUN
ejpam-1375	222	18	represents	represent	VERB
ejpam-1375	222	19	a	a	DET
ejpam-1375	222	20	different	different	ADJ
ejpam-1375	222	21	solution	solution	NOUN
ejpam-1375	222	22	to	to	ADP
ejpam-1375	222	23	a	a	DET
ejpam-1375	222	24	given	give	VERB
ejpam-1375	222	25	problem	problem	NOUN
ejpam-1375	222	26	.	.	PUNCT
ejpam-1375	223	1	it	it	PRON
ejpam-1375	223	2	follows	follow	VERB
ejpam-1375	223	3	the	the	DET
ejpam-1375	223	4	principles	principle	NOUN
ejpam-1375	223	5	first	first	ADV
ejpam-1375	223	6	laid	lay	VERB
ejpam-1375	223	7	down	down	ADP
ejpam-1375	223	8	by	by	ADP
ejpam-1375	223	9	charles	charles	PROPN
ejpam-1375	223	10	darwin	darwin	PROPN
ejpam-1375	223	11	of	of	ADP
ejpam-1375	223	12	survival	survival	NOUN
ejpam-1375	223	13	of	of	ADP
ejpam-1375	223	14	the	the	DET
ejpam-1375	223	15	fittest	fit	ADJ
ejpam-1375	223	16	.	.	PUNCT
ejpam-1375	224	1	the	the	DET
ejpam-1375	224	2	algorithm	algorithm	NOUN
ejpam-1375	224	3	searches	search	VERB
ejpam-1375	224	4	within	within	ADP
ejpam-1375	224	5	a	a	DET
ejpam-1375	224	6	defined	define	VERB
ejpam-1375	224	7	search	search	NOUN
ejpam-1375	224	8	space	space	NOUN
ejpam-1375	224	9	to	to	PART
ejpam-1375	224	10	solve	solve	VERB
ejpam-1375	224	11	a	a	DET
ejpam-1375	224	12	problem	problem	NOUN
ejpam-1375	224	13	.	.	PUNCT
ejpam-1375	225	1	it	it	PRON
ejpam-1375	225	2	has	have	VERB
ejpam-1375	225	3	outstanding	outstanding	ADJ
ejpam-1375	225	4	performance	performance	NOUN
ejpam-1375	225	5	in	in	ADP
ejpam-1375	225	6	finding	find	VERB
ejpam-1375	225	7	the	the	DET
ejpam-1375	225	8	optimal	optimal	ADJ
ejpam-1375	225	9	solution	solution	NOUN
ejpam-1375	225	10	for	for	ADP
ejpam-1375	225	11	problems	problem	NOUN
ejpam-1375	225	12	in	in	ADP
ejpam-1375	225	13	many	many	ADJ
ejpam-1375	225	14	different	different	ADJ
ejpam-1375	225	15	fields	field	NOUN
ejpam-1375	225	16	.	.	PUNCT
ejpam-1375	226	1	recall	recall	VERB
ejpam-1375	226	2	that	that	SCONJ
ejpam-1375	226	3	the	the	DET
ejpam-1375	226	4	regularized	regularize	VERB
ejpam-1375	226	5	regression	regression	NOUN
ejpam-1375	226	6	tree	tree	NOUN
ejpam-1375	226	7	and	and	CCONJ
ejpam-1375	226	8	rbf	rbf	PROPN
ejpam-1375	226	9	networks	network	NOUN
ejpam-1375	226	10	model	model	NOUN
ejpam-1375	226	11	given	give	VERB
ejpam-1375	226	12	by	by	ADP
ejpam-1375	226	13	1	1	NUM
ejpam-1375	226	14	,	,	PUNCT
ejpam-1375	226	15	the	the	DET
ejpam-1375	226	16	ga	ga	PROPN
ejpam-1375	226	17	is	be	AUX
ejpam-1375	226	18	used	use	VERB
ejpam-1375	226	19	to	to	PART
ejpam-1375	226	20	find	find	VERB
ejpam-1375	226	21	the	the	DET
ejpam-1375	226	22	best	good	ADJ
ejpam-1375	226	23	or	or	CCONJ
ejpam-1375	226	24	nearly	nearly	ADV
ejpam-1375	226	25	best	good	ADJ
ejpam-1375	226	26	subset	subset	NOUN
ejpam-1375	226	27	of	of	ADP
ejpam-1375	226	28	predictors	predictor	NOUN
ejpam-1375	226	29	from	from	ADP
ejpam-1375	226	30	the	the	DET
ejpam-1375	226	31	data	datum	NOUN
ejpam-1375	226	32	.	.	PUNCT
ejpam-1375	227	1	7.1	7.1	NUM
ejpam-1375	227	2	.	.	PUNCT
ejpam-1375	228	1	implementation	implementation	NOUN
ejpam-1375	228	2	of	of	ADP
ejpam-1375	228	3	the	the	DET
ejpam-1375	228	4	ga	ga	PROPN
ejpam-1375	228	5	the	the	DET
ejpam-1375	228	6	ga	ga	PROPN
ejpam-1375	228	7	is	be	AUX
ejpam-1375	228	8	implemented	implement	VERB
ejpam-1375	228	9	using	use	VERB
ejpam-1375	228	10	the	the	DET
ejpam-1375	228	11	following	follow	VERB
ejpam-1375	228	12	steps	step	NOUN
ejpam-1375	228	13	:	:	PUNCT
ejpam-1375	229	1	1	1	X
ejpam-1375	229	2	.	.	X
ejpam-1375	229	3	implementing	implement	VERB
ejpam-1375	229	4	a	a	DET
ejpam-1375	229	5	genetic	genetic	ADJ
ejpam-1375	229	6	coding	code	VERB
ejpam-1375	229	7	scheme	scheme	NOUN
ejpam-1375	229	8	:	:	PUNCT
ejpam-1375	229	9	the	the	DET
ejpam-1375	229	10	first	first	ADJ
ejpam-1375	229	11	step	step	NOUN
ejpam-1375	229	12	of	of	ADP
ejpam-1375	229	13	the	the	DET
ejpam-1375	229	14	ga	ga	PROPN
ejpam-1375	229	15	is	be	AUX
ejpam-1375	229	16	to	to	PART
ejpam-1375	229	17	represent	represent	VERB
ejpam-1375	229	18	each	each	DET
ejpam-1375	229	19	subset	subset	NOUN
ejpam-1375	229	20	model	model	NOUN
ejpam-1375	229	21	as	as	ADP
ejpam-1375	229	22	a	a	DET
ejpam-1375	229	23	binary	binary	ADJ
ejpam-1375	229	24	string	string	NOUN
ejpam-1375	229	25	.	.	PUNCT
ejpam-1375	230	1	a	a	DET
ejpam-1375	230	2	binary	binary	PROPN
ejpam-1375	230	3	code	code	NOUN
ejpam-1375	230	4	of	of	ADP
ejpam-1375	230	5	1	1	NUM
ejpam-1375	230	6	indicates	indicate	VERB
ejpam-1375	230	7	presence	presence	NOUN
ejpam-1375	230	8	and	and	CCONJ
ejpam-1375	230	9	a	a	DET
ejpam-1375	230	10	0	0	NUM
ejpam-1375	230	11	indicating	indicate	VERB
ejpam-1375	230	12	absence	absence	NOUN
ejpam-1375	230	13	.	.	PUNCT
ejpam-1375	231	1	every	every	DET
ejpam-1375	231	2	string	string	NOUN
ejpam-1375	231	3	is	be	AUX
ejpam-1375	231	4	of	of	ADP
ejpam-1375	231	5	the	the	DET
ejpam-1375	231	6	same	same	ADJ
ejpam-1375	231	7	length	length	NOUN
ejpam-1375	231	8	,	,	PUNCT
ejpam-1375	231	9	but	but	CCONJ
ejpam-1375	231	10	contain	contain	VERB
ejpam-1375	231	11	different	different	ADJ
ejpam-1375	231	12	combinations	combination	NOUN
ejpam-1375	231	13	of	of	ADP
ejpam-1375	231	14	predictor	predictor	NOUN
ejpam-1375	231	15	variables	variable	NOUN
ejpam-1375	231	16	.	.	PUNCT
ejpam-1375	232	1	for	for	ADP
ejpam-1375	232	2	a	a	DET
ejpam-1375	232	3	data	datum	NOUN
ejpam-1375	232	4	set	set	VERB
ejpam-1375	232	5	with	with	ADP
ejpam-1375	232	6	k	k	PROPN
ejpam-1375	232	7	=	=	SYM
ejpam-1375	232	8	6	6	NUM
ejpam-1375	232	9	predictors	predictor	NOUN
ejpam-1375	232	10	with	with	ADP
ejpam-1375	232	11	a	a	DET
ejpam-1375	232	12	constant	constant	ADJ
ejpam-1375	232	13	,	,	PUNCT
ejpam-1375	232	14	following	follow	VERB
ejpam-1375	232	15	string	string	NOUN
ejpam-1375	232	16	represents	represent	VERB
ejpam-1375	232	17	a	a	DET
ejpam-1375	232	18	model	model	NOUN
ejpam-1375	232	19	including	include	VERB
ejpam-1375	232	20	constant	constant	ADJ
ejpam-1375	232	21	,	,	PUNCT
ejpam-1375	232	22	and	and	CCONJ
ejpam-1375	232	23	input	input	NOUN
ejpam-1375	232	24	variables	variable	NOUN
ejpam-1375	232	25	x2	x2	PROPN
ejpam-1375	232	26	,	,	PUNCT
ejpam-1375	232	27	x3	x3	ADJ
ejpam-1375	232	28	,	,	PUNCT
ejpam-1375	232	29	and	and	CCONJ
ejpam-1375	232	30	x6	x6	PROPN
ejpam-1375	232	31	.	.	NOUN
ejpam-1375	233	1	1	1	NUM
ejpam-1375	233	2	0	0	NUM
ejpam-1375	233	3	1	1	NUM
ejpam-1375	233	4	1	1	NUM
ejpam-1375	233	5	0	0	NUM
ejpam-1375	233	6	0	0	NUM
ejpam-1375	233	7	1	1	NUM
ejpam-1375	233	8	x0	x0	NOUN
ejpam-1375	233	9	x1	x1	PROPN
ejpam-1375	234	1	x2	x2	NOUN
ejpam-1375	234	2	x3	x3	PROPN
ejpam-1375	234	3	x4	x4	PROPN
ejpam-1375	234	4	x5	x5	PROPN
ejpam-1375	234	5	x6	x6	PROPN
ejpam-1375	234	6	2	2	NUM
ejpam-1375	234	7	.	.	PUNCT
ejpam-1375	235	1	generating	generate	VERB
ejpam-1375	235	2	an	an	DET
ejpam-1375	235	3	initial	initial	ADJ
ejpam-1375	235	4	population	population	NOUN
ejpam-1375	235	5	of	of	ADP
ejpam-1375	235	6	the	the	DET
ejpam-1375	235	7	models	model	NOUN
ejpam-1375	235	8	:	:	PUNCT
ejpam-1375	235	9	the	the	DET
ejpam-1375	235	10	initial	initial	ADJ
ejpam-1375	235	11	population	population	NOUN
ejpam-1375	235	12	consists	consist	VERB
ejpam-1375	235	13	of	of	ADP
ejpam-1375	235	14	randomly	randomly	ADV
ejpam-1375	235	15	selected	select	VERB
ejpam-1375	235	16	models	model	NOUN
ejpam-1375	235	17	from	from	ADP
ejpam-1375	235	18	all	all	DET
ejpam-1375	235	19	possible	possible	ADJ
ejpam-1375	235	20	models	model	NOUN
ejpam-1375	235	21	.	.	PUNCT
ejpam-1375	236	1	we	we	PRON
ejpam-1375	236	2	have	have	VERB
ejpam-1375	236	3	to	to	PART
ejpam-1375	236	4	choose	choose	VERB
ejpam-1375	236	5	an	an	DET
ejpam-1375	236	6	initial	initial	ADJ
ejpam-1375	236	7	population	population	NOUN
ejpam-1375	236	8	of	of	ADP
ejpam-1375	236	9	size	size	NOUN
ejpam-1375	236	10	n	n	NOUN
ejpam-1375	236	11	.	.	PUNCT
ejpam-1375	237	1	our	our	PRON
ejpam-1375	237	2	algorithm	algorithm	NOUN
ejpam-1375	237	3	allows	allow	VERB
ejpam-1375	237	4	one	one	NUM
ejpam-1375	237	5	to	to	PART
ejpam-1375	237	6	choose	choose	VERB
ejpam-1375	237	7	any	any	DET
ejpam-1375	237	8	population	population	NOUN
ejpam-1375	237	9	size	size	NOUN
ejpam-1375	237	10	.	.	PUNCT
ejpam-1375	238	1	the	the	DET
ejpam-1375	238	2	best	good	ADJ
ejpam-1375	238	3	population	population	NOUN
ejpam-1375	238	4	size	size	NOUN
ejpam-1375	238	5	to	to	PART
ejpam-1375	238	6	choose	choose	VERB
ejpam-1375	238	7	depends	depend	VERB
ejpam-1375	238	8	on	on	ADP
ejpam-1375	238	9	many	many	ADJ
ejpam-1375	238	10	different	different	ADJ
ejpam-1375	238	11	factors	factor	NOUN
ejpam-1375	238	12	and	and	CCONJ
ejpam-1375	238	13	requires	require	VERB
ejpam-1375	238	14	further	further	ADJ
ejpam-1375	238	15	investigation	investigation	NOUN
ejpam-1375	238	16	.	.	PUNCT
ejpam-1375	239	1	3	3	X
ejpam-1375	239	2	.	.	X
ejpam-1375	239	3	using	use	VERB
ejpam-1375	239	4	a	a	DET
ejpam-1375	239	5	fitness	fitness	NOUN
ejpam-1375	239	6	function	function	NOUN
ejpam-1375	239	7	to	to	PART
ejpam-1375	239	8	evaluate	evaluate	VERB
ejpam-1375	239	9	the	the	DET
ejpam-1375	239	10	performance	performance	NOUN
ejpam-1375	239	11	of	of	ADP
ejpam-1375	239	12	the	the	DET
ejpam-1375	239	13	models	model	NOUN
ejpam-1375	239	14	in	in	ADP
ejpam-1375	239	15	the	the	DET
ejpam-1375	239	16	population	population	NOUN
ejpam-1375	239	17	:	:	PUNCT
ejpam-1375	239	18	a	a	DET
ejpam-1375	239	19	fitness	fitness	NOUN
ejpam-1375	239	20	function	function	NOUN
ejpam-1375	239	21	provides	provide	VERB
ejpam-1375	239	22	a	a	DET
ejpam-1375	239	23	way	way	NOUN
ejpam-1375	239	24	of	of	ADP
ejpam-1375	239	25	evaluating	evaluate	VERB
ejpam-1375	239	26	the	the	DET
ejpam-1375	239	27	performance	performance	NOUN
ejpam-1375	239	28	of	of	ADP
ejpam-1375	239	29	the	the	DET
ejpam-1375	239	30	models	model	NOUN
ejpam-1375	239	31	.	.	PUNCT
ejpam-1375	240	1	we	we	PRON
ejpam-1375	240	2	use	use	VERB
ejpam-1375	240	3	the	the	DET
ejpam-1375	240	4	icom	icom	PROPN
ejpam-1375	240	5	p	p	PROPN
ejpam-1375	240	6	information	information	NOUN
ejpam-1375	240	7	criteria	criterion	NOUN
ejpam-1375	240	8	defined	define	VERB
ejpam-1375	240	9	in	in	ADP
ejpam-1375	240	10	the	the	DET
ejpam-1375	240	11	previous	previous	ADJ
ejpam-1375	240	12	section	section	NOUN
ejpam-1375	240	13	as	as	ADP
ejpam-1375	240	14	the	the	DET
ejpam-1375	240	15	fitness	fitness	NOUN
ejpam-1375	240	16	function	function	NOUN
ejpam-1375	240	17	.	.	PUNCT
ejpam-1375	241	1	in	in	ADP
ejpam-1375	241	2	general	general	ADJ
ejpam-1375	241	3	,	,	PUNCT
ejpam-1375	241	4	the	the	DET
ejpam-1375	241	5	analyst	analyst	NOUN
ejpam-1375	241	6	has	have	VERB
ejpam-1375	241	7	the	the	DET
ejpam-1375	241	8	freedom	freedom	NOUN
ejpam-1375	241	9	of	of	ADP
ejpam-1375	241	10	using	use	VERB
ejpam-1375	241	11	any	any	DET
ejpam-1375	241	12	appropriate	appropriate	ADJ
ejpam-1375	241	13	model	model	NOUN
ejpam-1375	241	14	selection	selection	NOUN
ejpam-1375	241	15	criterion	criterion	NOUN
ejpam-1375	241	16	as	as	ADP
ejpam-1375	241	17	the	the	DET
ejpam-1375	241	18	fitness	fitness	NOUN
ejpam-1375	241	19	functions	function	NOUN
ejpam-1375	241	20	.	.	PUNCT
ejpam-1375	242	1	4	4	X
ejpam-1375	242	2	.	.	X
ejpam-1375	242	3	selecting	select	VERB
ejpam-1375	242	4	the	the	DET
ejpam-1375	242	5	parents	parent	NOUN
ejpam-1375	242	6	models	model	NOUN
ejpam-1375	242	7	from	from	ADP
ejpam-1375	242	8	the	the	DET
ejpam-1375	242	9	current	current	ADJ
ejpam-1375	242	10	population	population	NOUN
ejpam-1375	242	11	:	:	PUNCT
ejpam-1375	242	12	this	this	DET
ejpam-1375	242	13	step	step	NOUN
ejpam-1375	242	14	is	be	AUX
ejpam-1375	242	15	to	to	PART
ejpam-1375	242	16	choose	choose	VERB
ejpam-1375	242	17	models	model	NOUN
ejpam-1375	242	18	to	to	PART
ejpam-1375	242	19	be	be	AUX
ejpam-1375	242	20	used	use	VERB
ejpam-1375	242	21	in	in	ADP
ejpam-1375	242	22	the	the	DET
ejpam-1375	242	23	next	next	ADJ
ejpam-1375	242	24	step	step	NOUN
ejpam-1375	242	25	to	to	PART
ejpam-1375	242	26	generate	generate	VERB
ejpam-1375	242	27	new	new	ADJ
ejpam-1375	242	28	population	population	NOUN
ejpam-1375	242	29	.	.	PUNCT
ejpam-1375	243	1	the	the	DET
ejpam-1375	243	2	selection	selection	NOUN
ejpam-1375	243	3	of	of	ADP
ejpam-1375	243	4	parents	parent	NOUN
ejpam-1375	243	5	’	'	PUNCT
ejpam-1375	243	6	models	model	NOUN
ejpam-1375	243	7	is	be	AUX
ejpam-1375	243	8	based	base	VERB
ejpam-1375	243	9	on	on	ADP
ejpam-1375	243	10	the	the	DET
ejpam-1375	243	11	natural	natural	ADJ
ejpam-1375	243	12	selection	selection	NOUN
ejpam-1375	243	13	.	.	PUNCT
ejpam-1375	244	1	that	that	PRON
ejpam-1375	244	2	is	be	AUX
ejpam-1375	244	3	,	,	PUNCT
ejpam-1375	244	4	the	the	DET
ejpam-1375	244	5	model	model	NOUN
ejpam-1375	244	6	with	with	ADP
ejpam-1375	244	7	better	well	ADJ
ejpam-1375	244	8	fitness	fitness	NOUN
ejpam-1375	244	9	value	value	NOUN
ejpam-1375	244	10	has	have	VERB
ejpam-1375	244	11	greater	great	ADJ
ejpam-1375	244	12	chance	chance	NOUN
ejpam-1375	244	13	to	to	PART
ejpam-1375	244	14	be	be	AUX
ejpam-1375	244	15	selected	select	VERB
ejpam-1375	244	16	as	as	ADP
ejpam-1375	244	17	parents	parent	NOUN
ejpam-1375	244	18	.	.	PUNCT
ejpam-1375	245	1	we	we	PRON
ejpam-1375	245	2	calculate	calculate	VERB
ejpam-1375	245	3	the	the	DET
ejpam-1375	245	4	difference	difference	NOUN
ejpam-1375	245	5	:	:	PUNCT
ejpam-1375	245	6	∆icom	∆icom	X
ejpam-1375	245	7	p(i)(i	p(i)(i	X
ejpam-1375	245	8	f	f	X
ejpam-1375	245	9	i	i	PROPN
ejpam-1375	245	10	m	m	PROPN
ejpam-1375	245	11	)	)	PUNCT
ejpam-1375	245	12	=	=	SYM
ejpam-1375	245	13	icom	icom	PROPN
ejpam-1375	245	14	p(i	p(i	PROPN
ejpam-1375	245	15	f	f	PROPN
ejpam-1375	245	16	im)max	im)max	PROPN
ejpam-1375	245	17	−	−	PROPN
ejpam-1375	245	18	icom	icom	PROPN
ejpam-1375	245	19	p(i	p(i	PROPN
ejpam-1375	245	20	f	f	PROPN
ejpam-1375	245	21	im)i	im)i	PROPN
ejpam-1375	245	22	=	=	PUNCT
ejpam-1375	245	23	range	range	NOUN
ejpam-1375	245	24	(	(	PUNCT
ejpam-1375	245	25	40	40	NUM
ejpam-1375	245	26	)	)	PUNCT
ejpam-1375	245	27	for	for	ADP
ejpam-1375	245	28	i	i	X
ejpam-1375	245	29	=	=	SYM
ejpam-1375	245	30	1,2	1,2	NUM
ejpam-1375	245	31	,	,	PUNCT
ejpam-1375	245	32	.	.	PUNCT
ejpam-1375	245	33	.	.	PUNCT
ejpam-1375	245	34	.	.	PUNCT
ejpam-1375	245	35	,	,	PUNCT
ejpam-1375	245	36	n	n	X
ejpam-1375	245	37	,	,	PUNCT
ejpam-1375	245	38	where	where	SCONJ
ejpam-1375	245	39	n	n	PRON
ejpam-1375	245	40	is	be	AUX
ejpam-1375	245	41	the	the	DET
ejpam-1375	245	42	population	population	NOUN
ejpam-1375	245	43	size	size	NOUN
ejpam-1375	245	44	.	.	PUNCT
ejpam-1375	246	1	next	next	ADV
ejpam-1375	246	2	,	,	PUNCT
ejpam-1375	246	3	we	we	PRON
ejpam-1375	246	4	average	average	VERB
ejpam-1375	246	5	these	these	DET
ejpam-1375	246	6	differences	difference	NOUN
ejpam-1375	246	7	;	;	PUNCT
ejpam-1375	246	8	that	that	PRON
ejpam-1375	246	9	is	is	ADV
ejpam-1375	246	10	,	,	PUNCT
ejpam-1375	246	11	we	we	PRON
ejpam-1375	246	12	compute	compute	VERB
ejpam-1375	246	13	∆icom	∆icom	ADJ
ejpam-1375	247	1	p(i	p(i	PROPN
ejpam-1375	248	1	f	f	PROPN
ejpam-1375	249	1	i	i	PRON
ejpam-1375	249	2	m	m	VERB
ejpam-1375	249	3	)	)	PUNCT
ejpam-1375	250	1	=	=	SYM
ejpam-1375	250	2	1	1	NUM
ejpam-1375	251	1	n	n	NUM
ejpam-1375	251	2	n∑	n∑	NOUN
ejpam-1375	251	3	i=1	i=1	PROPN
ejpam-1375	251	4	∆icom	∆icom	X
ejpam-1375	251	5	p(i)(i	p(i)(i	X
ejpam-1375	251	6	f	f	X
ejpam-1375	251	7	i	i	PROPN
ejpam-1375	251	8	m	m	PROPN
ejpam-1375	251	9	)	)	PUNCT
ejpam-1375	251	10	(	(	PUNCT
ejpam-1375	251	11	41	41	NUM
ejpam-1375	251	12	)	)	PUNCT
ejpam-1375	251	13	o.	o.	NOUN
ejpam-1375	251	14	akbilgic	akbilgic	PROPN
ejpam-1375	251	15	,	,	PUNCT
ejpam-1375	251	16	h.	h.	PROPN
ejpam-1375	251	17	bozdogan	bozdogan	PROPN
ejpam-1375	251	18	/	/	SYM
ejpam-1375	251	19	eur	eur	PROPN
ejpam-1375	251	20	.	.	PUNCT
ejpam-1375	252	1	j.	j.	PROPN
ejpam-1375	252	2	pure	pure	PROPN
ejpam-1375	252	3	appl	appl	PROPN
ejpam-1375	252	4	.	.	PROPN
ejpam-1375	252	5	math	math	PROPN
ejpam-1375	252	6	,	,	PUNCT
ejpam-1375	252	7	4	4	NUM
ejpam-1375	252	8	(	(	PUNCT
ejpam-1375	252	9	2011	2011	NUM
ejpam-1375	252	10	)	)	PUNCT
ejpam-1375	252	11	,	,	PUNCT
ejpam-1375	252	12	467	467	NUM
ejpam-1375	252	13	-	-	SYM
ejpam-1375	252	14	485	485	NUM
ejpam-1375	252	15	478	478	NUM
ejpam-1375	252	16	then	then	ADV
ejpam-1375	252	17	the	the	DET
ejpam-1375	252	18	ratio	ratio	NOUN
ejpam-1375	252	19	of	of	ADP
ejpam-1375	252	20	each	each	DET
ejpam-1375	252	21	model	model	NOUN
ejpam-1375	252	22	’s	’s	PART
ejpam-1375	252	23	difference	difference	NOUN
ejpam-1375	252	24	value	value	NOUN
ejpam-1375	252	25	to	to	ADP
ejpam-1375	252	26	the	the	DET
ejpam-1375	252	27	mean	mean	ADJ
ejpam-1375	252	28	difference	difference	NOUN
ejpam-1375	252	29	value	value	NOUN
ejpam-1375	252	30	is	be	AUX
ejpam-1375	252	31	calculated	calculate	VERB
ejpam-1375	252	32	.	.	PUNCT
ejpam-1375	253	1	that	that	PRON
ejpam-1375	253	2	is	is	ADV
ejpam-1375	253	3	,	,	PUNCT
ejpam-1375	253	4	we	we	PRON
ejpam-1375	253	5	compute	compute	VERB
ejpam-1375	253	6	icom	icom	PROPN
ejpam-1375	253	7	pratio	pratio	NOUN
ejpam-1375	253	8	=	=	X
ejpam-1375	253	9	∆icom	∆icom	X
ejpam-1375	253	10	p(i)(i	p(i)(i	X
ejpam-1375	253	11	f	f	X
ejpam-1375	253	12	i	i	PROPN
ejpam-1375	253	13	m	m	VERB
ejpam-1375	253	14	)	)	PUNCT
ejpam-1375	253	15	∆icom	∆icom	PART
ejpam-1375	254	1	p(i	p(i	PROPN
ejpam-1375	254	2	f	f	PROPN
ejpam-1375	254	3	i	i	PRON
ejpam-1375	254	4	m	m	VERB
ejpam-1375	254	5	)	)	PUNCT
ejpam-1375	254	6	(	(	PUNCT
ejpam-1375	254	7	42	42	X
ejpam-1375	254	8	)	)	PUNCT
ejpam-1375	254	9	this	this	DET
ejpam-1375	254	10	ratio	ratio	NOUN
ejpam-1375	254	11	is	be	AUX
ejpam-1375	254	12	used	use	VERB
ejpam-1375	254	13	to	to	PART
ejpam-1375	254	14	determine	determine	VERB
ejpam-1375	254	15	which	which	DET
ejpam-1375	254	16	models	model	NOUN
ejpam-1375	254	17	will	will	AUX
ejpam-1375	254	18	be	be	AUX
ejpam-1375	254	19	included	include	VERB
ejpam-1375	254	20	in	in	ADP
ejpam-1375	254	21	the	the	DET
ejpam-1375	254	22	mating	mating	NOUN
ejpam-1375	254	23	pool	pool	NOUN
ejpam-1375	254	24	.	.	PUNCT
ejpam-1375	255	1	the	the	DET
ejpam-1375	255	2	chance	chance	NOUN
ejpam-1375	255	3	of	of	ADP
ejpam-1375	255	4	a	a	DET
ejpam-1375	255	5	model	model	NOUN
ejpam-1375	255	6	being	be	AUX
ejpam-1375	255	7	mated	mate	VERB
ejpam-1375	255	8	is	be	AUX
ejpam-1375	255	9	proportional	proportional	ADJ
ejpam-1375	255	10	to	to	ADP
ejpam-1375	255	11	this	this	DET
ejpam-1375	255	12	ratio	ratio	NOUN
ejpam-1375	255	13	.	.	PUNCT
ejpam-1375	256	1	in	in	ADP
ejpam-1375	256	2	other	other	ADJ
ejpam-1375	256	3	words	word	NOUN
ejpam-1375	256	4	,	,	PUNCT
ejpam-1375	256	5	a	a	DET
ejpam-1375	256	6	model	model	NOUN
ejpam-1375	256	7	with	with	ADP
ejpam-1375	256	8	a	a	DET
ejpam-1375	256	9	ratio	ratio	NOUN
ejpam-1375	256	10	of	of	ADP
ejpam-1375	256	11	two	two	NUM
ejpam-1375	256	12	is	be	AUX
ejpam-1375	256	13	twice	twice	ADV
ejpam-1375	256	14	as	as	ADV
ejpam-1375	256	15	likely	likely	ADJ
ejpam-1375	256	16	to	to	PART
ejpam-1375	256	17	mate	mate	VERB
ejpam-1375	256	18	as	as	ADP
ejpam-1375	256	19	a	a	DET
ejpam-1375	256	20	model	model	NOUN
ejpam-1375	256	21	with	with	ADP
ejpam-1375	256	22	a	a	DET
ejpam-1375	256	23	ratio	ratio	NOUN
ejpam-1375	256	24	of	of	ADP
ejpam-1375	256	25	one	one	NUM
ejpam-1375	256	26	.	.	PUNCT
ejpam-1375	257	1	the	the	DET
ejpam-1375	257	2	process	process	NOUN
ejpam-1375	257	3	of	of	ADP
ejpam-1375	257	4	selecting	selecting	NOUN
ejpam-1375	257	5	mates	mate	NOUN
ejpam-1375	257	6	to	to	PART
ejpam-1375	257	7	produce	produce	VERB
ejpam-1375	257	8	offspring	offspring	NOUN
ejpam-1375	257	9	models	model	NOUN
ejpam-1375	257	10	continues	continue	VERB
ejpam-1375	257	11	until	until	SCONJ
ejpam-1375	257	12	the	the	DET
ejpam-1375	257	13	number	number	NOUN
ejpam-1375	257	14	of	of	ADP
ejpam-1375	257	15	offsprings	offspring	NOUN
ejpam-1375	257	16	equals	equal	VERB
ejpam-1375	257	17	the	the	DET
ejpam-1375	257	18	initial	initial	ADJ
ejpam-1375	257	19	population	population	NOUN
ejpam-1375	257	20	size	size	NOUN
ejpam-1375	257	21	.	.	PUNCT
ejpam-1375	258	1	this	this	PRON
ejpam-1375	258	2	is	be	AUX
ejpam-1375	258	3	called	call	VERB
ejpam-1375	258	4	the	the	DET
ejpam-1375	258	5	proportional	proportional	ADJ
ejpam-1375	258	6	selection	selection	NOUN
ejpam-1375	258	7	or	or	CCONJ
ejpam-1375	258	8	fitting	fitting	NOUN
ejpam-1375	258	9	.	.	PUNCT
ejpam-1375	259	1	5	5	X
ejpam-1375	259	2	.	.	X
ejpam-1375	259	3	produce	produce	VERB
ejpam-1375	259	4	offspring	offspring	NOUN
ejpam-1375	259	5	models	model	NOUN
ejpam-1375	259	6	by	by	ADP
ejpam-1375	259	7	crossover	crossover	NOUN
ejpam-1375	259	8	and	and	CCONJ
ejpam-1375	259	9	mutation	mutation	NOUN
ejpam-1375	259	10	process	process	NOUN
ejpam-1375	259	11	:	:	PUNCT
ejpam-1375	259	12	the	the	DET
ejpam-1375	259	13	selected	select	VERB
ejpam-1375	259	14	parents	parent	NOUN
ejpam-1375	259	15	are	be	AUX
ejpam-1375	259	16	then	then	ADV
ejpam-1375	259	17	used	use	VERB
ejpam-1375	259	18	to	to	PART
ejpam-1375	259	19	generate	generate	VERB
ejpam-1375	259	20	offsprings	offspring	NOUN
ejpam-1375	259	21	by	by	ADP
ejpam-1375	259	22	performing	perform	VERB
ejpam-1375	259	23	crossover	crossover	NOUN
ejpam-1375	259	24	and/or	and/or	CCONJ
ejpam-1375	259	25	mutation	mutation	NOUN
ejpam-1375	259	26	process	process	NOUN
ejpam-1375	259	27	on	on	ADP
ejpam-1375	259	28	them	they	PRON
ejpam-1375	259	29	.	.	PUNCT
ejpam-1375	260	1	both	both	CCONJ
ejpam-1375	260	2	the	the	DET
ejpam-1375	260	3	crossover	crossover	NOUN
ejpam-1375	260	4	and	and	CCONJ
ejpam-1375	260	5	mutation	mutation	NOUN
ejpam-1375	260	6	probability	probability	NOUN
ejpam-1375	260	7	is	be	AUX
ejpam-1375	260	8	determined	determine	VERB
ejpam-1375	260	9	by	by	ADP
ejpam-1375	260	10	the	the	DET
ejpam-1375	260	11	analyst	analyst	NOUN
ejpam-1375	260	12	.	.	PUNCT
ejpam-1375	261	1	a	a	DET
ejpam-1375	261	2	higher	high	ADJ
ejpam-1375	261	3	crossover	crossover	NOUN
ejpam-1375	261	4	probability	probability	NOUN
ejpam-1375	261	5	will	will	AUX
ejpam-1375	261	6	on	on	ADP
ejpam-1375	261	7	one	one	NUM
ejpam-1375	261	8	hand	hand	NOUN
ejpam-1375	261	9	introduce	introduce	VERB
ejpam-1375	261	10	more	more	ADJ
ejpam-1375	261	11	new	new	ADJ
ejpam-1375	261	12	models	model	NOUN
ejpam-1375	261	13	into	into	ADP
ejpam-1375	261	14	the	the	DET
ejpam-1375	261	15	population	population	NOUN
ejpam-1375	261	16	in	in	ADP
ejpam-1375	261	17	each	each	DET
ejpam-1375	261	18	generation	generation	NOUN
ejpam-1375	261	19	,	,	PUNCT
ejpam-1375	261	20	while	while	SCONJ
ejpam-1375	261	21	on	on	ADP
ejpam-1375	261	22	the	the	DET
ejpam-1375	261	23	other	other	ADJ
ejpam-1375	261	24	hand	hand	NOUN
ejpam-1375	261	25	remove	remove	VERB
ejpam-1375	261	26	more	more	ADJ
ejpam-1375	261	27	of	of	ADP
ejpam-1375	261	28	the	the	DET
ejpam-1375	261	29	good	good	ADJ
ejpam-1375	261	30	models	model	NOUN
ejpam-1375	261	31	from	from	ADP
ejpam-1375	261	32	the	the	DET
ejpam-1375	261	33	previous	previous	ADJ
ejpam-1375	261	34	generation	generation	NOUN
ejpam-1375	261	35	.	.	PUNCT
ejpam-1375	262	1	a	a	DET
ejpam-1375	262	2	mutation	mutation	NOUN
ejpam-1375	262	3	probability	probability	NOUN
ejpam-1375	262	4	is	be	AUX
ejpam-1375	262	5	a	a	DET
ejpam-1375	262	6	random	random	ADJ
ejpam-1375	262	7	search	search	NOUN
ejpam-1375	262	8	operator	operator	NOUN
ejpam-1375	262	9	.	.	PUNCT
ejpam-1375	263	1	it	it	PRON
ejpam-1375	263	2	helps	help	VERB
ejpam-1375	263	3	to	to	PART
ejpam-1375	263	4	jump	jump	VERB
ejpam-1375	263	5	to	to	ADP
ejpam-1375	263	6	another	another	DET
ejpam-1375	263	7	search	search	NOUN
ejpam-1375	263	8	space	space	NOUN
ejpam-1375	263	9	within	within	ADP
ejpam-1375	263	10	the	the	DET
ejpam-1375	263	11	solutions	solution	NOUN
ejpam-1375	263	12	’	'	PUNCT
ejpam-1375	263	13	scope	scope	NOUN
ejpam-1375	263	14	.	.	PUNCT
ejpam-1375	264	1	[	[	X
ejpam-1375	264	2	15	15	NUM
ejpam-1375	264	3	]	]	PUNCT
ejpam-1375	264	4	states	state	VERB
ejpam-1375	264	5	that	that	SCONJ
ejpam-1375	264	6	mutation	mutation	NOUN
ejpam-1375	264	7	should	should	AUX
ejpam-1375	264	8	be	be	AUX
ejpam-1375	264	9	used	use	VERB
ejpam-1375	264	10	sparingly	sparingly	ADV
ejpam-1375	264	11	because	because	SCONJ
ejpam-1375	264	12	the	the	DET
ejpam-1375	264	13	algorithm	algorithm	NOUN
ejpam-1375	264	14	will	will	AUX
ejpam-1375	264	15	become	become	VERB
ejpam-1375	264	16	little	little	ADV
ejpam-1375	264	17	more	more	ADJ
ejpam-1375	264	18	than	than	ADP
ejpam-1375	264	19	a	a	DET
ejpam-1375	264	20	random	random	ADJ
ejpam-1375	264	21	search	search	NOUN
ejpam-1375	264	22	with	with	ADP
ejpam-1375	264	23	a	a	DET
ejpam-1375	264	24	high	high	ADJ
ejpam-1375	264	25	mutation	mutation	NOUN
ejpam-1375	264	26	probability	probability	NOUN
ejpam-1375	264	27	.	.	PUNCT
ejpam-1375	265	1	there	there	PRON
ejpam-1375	265	2	are	be	VERB
ejpam-1375	265	3	several	several	ADJ
ejpam-1375	265	4	different	different	ADJ
ejpam-1375	265	5	ways	way	NOUN
ejpam-1375	265	6	of	of	ADP
ejpam-1375	265	7	performing	perform	VERB
ejpam-1375	265	8	the	the	DET
ejpam-1375	265	9	crossover	crossover	NOUN
ejpam-1375	265	10	.	.	PUNCT
ejpam-1375	266	1	these	these	PRON
ejpam-1375	266	2	are	be	AUX
ejpam-1375	266	3	single	single	ADJ
ejpam-1375	266	4	point	point	NOUN
ejpam-1375	266	5	crossover	crossover	NOUN
ejpam-1375	266	6	,	,	PUNCT
ejpam-1375	266	7	two	two	NUM
ejpam-1375	266	8	-	-	PUNCT
ejpam-1375	266	9	point	point	NOUN
ejpam-1375	266	10	crossover	crossover	NOUN
ejpam-1375	266	11	,	,	PUNCT
ejpam-1375	266	12	and	and	CCONJ
ejpam-1375	266	13	uniform	uniform	ADJ
ejpam-1375	266	14	crossover	crossover	NOUN
ejpam-1375	266	15	,	,	PUNCT
ejpam-1375	266	16	etc	etc	X
ejpam-1375	266	17	.	.	X
ejpam-1375	266	18	8	8	X
ejpam-1375	266	19	.	.	PUNCT
ejpam-1375	267	1	simulation	simulation	NOUN
ejpam-1375	267	2	studies	study	NOUN
ejpam-1375	267	3	in	in	ADP
ejpam-1375	267	4	this	this	DET
ejpam-1375	267	5	section	section	NOUN
ejpam-1375	267	6	,	,	PUNCT
ejpam-1375	267	7	we	we	PRON
ejpam-1375	267	8	report	report	VERB
ejpam-1375	267	9	our	our	PRON
ejpam-1375	267	10	computational	computational	ADJ
ejpam-1375	267	11	results	result	NOUN
ejpam-1375	267	12	on	on	ADP
ejpam-1375	267	13	a	a	DET
ejpam-1375	267	14	simulated	simulate	VERB
ejpam-1375	267	15	data	datum	NOUN
ejpam-1375	267	16	set	set	VERB
ejpam-1375	267	17	using	use	VERB
ejpam-1375	267	18	hybrid	hybrid	ADJ
ejpam-1375	267	19	rbf	rbf	PROPN
ejpam-1375	267	20	-	-	PUNCT
ejpam-1375	267	21	nn	nn	PROPN
ejpam-1375	267	22	approach	approach	NOUN
ejpam-1375	267	23	between	between	ADP
ejpam-1375	267	24	the	the	DET
ejpam-1375	267	25	regression	regression	NOUN
ejpam-1375	267	26	trees	trees	PROPN
ejpam-1375	267	27	rbf	rbf	PROPN
ejpam-1375	267	28	networks	network	NOUN
ejpam-1375	267	29	with	with	ADP
ejpam-1375	267	30	regularization	regularization	NOUN
ejpam-1375	267	31	,	,	PUNCT
ejpam-1375	267	32	the	the	DET
ejpam-1375	267	33	ga	ga	PROPN
ejpam-1375	267	34	and	and	CCONJ
ejpam-1375	267	35	icom	icom	PROPN
ejpam-1375	267	36	p(i	p(i	PROPN
ejpam-1375	267	37	f	f	PROPN
ejpam-1375	267	38	im)misspec	im)misspec	VERB
ejpam-1375	267	39	.	.	PUNCT
ejpam-1375	268	1	in	in	ADP
ejpam-1375	268	2	our	our	PRON
ejpam-1375	268	3	numerical	numerical	ADJ
ejpam-1375	268	4	example	example	NOUN
ejpam-1375	268	5	,	,	PUNCT
ejpam-1375	268	6	we	we	PRON
ejpam-1375	268	7	use	use	VERB
ejpam-1375	268	8	different	different	ADJ
ejpam-1375	268	9	basis	basis	NOUN
ejpam-1375	268	10	kernels	kernel	NOUN
ejpam-1375	268	11	,	,	PUNCT
ejpam-1375	268	12	gaussian	gaussian	ADJ
ejpam-1375	268	13	kernel	kernel	NOUN
ejpam-1375	268	14	(	(	PUNCT
ejpam-1375	268	15	gk	gk	PROPN
ejpam-1375	268	16	)	)	PUNCT
ejpam-1375	268	17	,	,	PUNCT
ejpam-1375	268	18	cauchy	cauchy	PROPN
ejpam-1375	268	19	kernel	kernel	PROPN
ejpam-1375	268	20	(	(	PUNCT
ejpam-1375	268	21	ck	ck	NOUN
ejpam-1375	268	22	)	)	PUNCT
ejpam-1375	268	23	,	,	PUNCT
ejpam-1375	268	24	multiquadric	multiquadric	ADJ
ejpam-1375	268	25	kernel	kernel	PROPN
ejpam-1375	268	26	(	(	PUNCT
ejpam-1375	268	27	mlqk	mlqk	PROPN
ejpam-1375	268	28	)	)	PUNCT
ejpam-1375	268	29	,	,	PUNCT
ejpam-1375	268	30	and	and	CCONJ
ejpam-1375	268	31	inverse	inverse	NOUN
ejpam-1375	268	32	multiquadric	multiquadric	ADJ
ejpam-1375	268	33	kernel	kernel	PROPN
ejpam-1375	268	34	(	(	PUNCT
ejpam-1375	268	35	imlqk	imlqk	NOUN
ejpam-1375	268	36	)	)	PUNCT
ejpam-1375	268	37	.	.	PUNCT
ejpam-1375	269	1	on	on	ADP
ejpam-1375	269	2	the	the	DET
ejpam-1375	269	3	other	other	ADJ
ejpam-1375	269	4	hand	hand	NOUN
ejpam-1375	269	5	,	,	PUNCT
ejpam-1375	269	6	to	to	PART
ejpam-1375	269	7	choose	choose	VERB
ejpam-1375	269	8	the	the	DET
ejpam-1375	269	9	optimal	optimal	ADJ
ejpam-1375	269	10	ridge	ridge	NOUN
ejpam-1375	269	11	parameter	parameter	PROPN
ejpam-1375	269	12	λ	λ	PROPN
ejpam-1375	269	13	for	for	ADP
ejpam-1375	269	14	the	the	DET
ejpam-1375	269	15	regularization	regularization	NOUN
ejpam-1375	269	16	,	,	PUNCT
ejpam-1375	269	17	we	we	PRON
ejpam-1375	269	18	use	use	VERB
ejpam-1375	269	19	hoerl	hoerl	NOUN
ejpam-1375	269	20	,	,	PUNCT
ejpam-1375	269	21	kennard	kennard	PROPN
ejpam-1375	269	22	&	&	CCONJ
ejpam-1375	269	23	baldwin	baldwin	PROPN
ejpam-1375	269	24	(	(	PUNCT
ejpam-1375	269	25	hkb	hkb	PROPN
ejpam-1375	269	26	)	)	PUNCT
ejpam-1375	269	27	method	method	NOUN
ejpam-1375	269	28	under	under	ADP
ejpam-1375	269	29	four	four	NUM
ejpam-1375	269	30	different	different	ADJ
ejpam-1375	269	31	model	model	NOUN
ejpam-1375	269	32	selection	selection	NOUN
ejpam-1375	269	33	criteria	criterion	NOUN
ejpam-1375	269	34	.	.	PUNCT
ejpam-1375	270	1	namely	namely	ADV
ejpam-1375	270	2	,	,	PUNCT
ejpam-1375	270	3	we	we	PRON
ejpam-1375	270	4	use	use	VERB
ejpam-1375	270	5	aic	aic	PROPN
ejpam-1375	270	6	,	,	PUNCT
ejpam-1375	270	7	sbc	sbc	PROPN
ejpam-1375	270	8	,	,	PUNCT
ejpam-1375	270	9	caic	caic	PROPN
ejpam-1375	270	10	f	f	PROPN
ejpam-1375	270	11	,	,	PUNCT
ejpam-1375	270	12	and	and	CCONJ
ejpam-1375	270	13	icom	icom	PROPN
ejpam-1375	270	14	p(i	p(i	PROPN
ejpam-1375	270	15	f	f	PROPN
ejpam-1375	270	16	im)misspec	im)misspec	VERB
ejpam-1375	270	17	.	.	PUNCT
ejpam-1375	271	1	we	we	PRON
ejpam-1375	271	2	define	define	VERB
ejpam-1375	271	3	regression	regression	NOUN
ejpam-1375	271	4	tree	tree	NOUN
ejpam-1375	271	5	parameters	parameter	NOUN
ejpam-1375	271	6	;	;	PUNCT
ejpam-1375	271	7	pmin	pmin	NOUN
ejpam-1375	271	8	is	be	AUX
ejpam-1375	271	9	integer	integer	NOUN
ejpam-1375	271	10	value	value	NOUN
ejpam-1375	271	11	of	of	ADP
ejpam-1375	271	12	10	10	NUM
ejpam-1375	271	13	%	%	NOUN
ejpam-1375	271	14	of	of	ADP
ejpam-1375	271	15	training	training	NOUN
ejpam-1375	271	16	data	datum	NOUN
ejpam-1375	271	17	sample	sample	NOUN
ejpam-1375	271	18	size	size	NOUN
ejpam-1375	271	19	,	,	PUNCT
ejpam-1375	271	20	α	α	PROPN
ejpam-1375	271	21	parameter	parameter	NOUN
ejpam-1375	271	22	is	be	AUX
ejpam-1375	271	23	2	2	NUM
ejpam-1375	271	24	or	or	CCONJ
ejpam-1375	271	25	4	4	NUM
ejpam-1375	271	26	whichever	whichever	PRON
ejpam-1375	271	27	fits	fit	VERB
ejpam-1375	271	28	better	well	ADV
ejpam-1375	271	29	.	.	PUNCT
ejpam-1375	272	1	the	the	DET
ejpam-1375	272	2	ga	ga	PROPN
ejpam-1375	272	3	parameters	parameter	NOUN
ejpam-1375	272	4	are	be	AUX
ejpam-1375	272	5	:	:	PUNCT
ejpam-1375	272	6	number	number	NOUN
ejpam-1375	272	7	of	of	ADP
ejpam-1375	272	8	generations	generation	NOUN
ejpam-1375	272	9	is	be	AUX
ejpam-1375	272	10	15	15	NUM
ejpam-1375	272	11	,	,	PUNCT
ejpam-1375	272	12	population	population	NOUN
ejpam-1375	272	13	size	size	NOUN
ejpam-1375	272	14	is	be	AUX
ejpam-1375	272	15	10	10	NUM
ejpam-1375	272	16	,	,	PUNCT
ejpam-1375	272	17	crossover	crossover	ADP
ejpam-1375	272	18	type	type	NOUN
ejpam-1375	272	19	is	be	AUX
ejpam-1375	272	20	uniform	uniform	ADJ
ejpam-1375	272	21	,	,	PUNCT
ejpam-1375	272	22	probability	probability	NOUN
ejpam-1375	272	23	of	of	ADP
ejpam-1375	272	24	crossover	crossover	NOUN
ejpam-1375	272	25	is	be	AUX
ejpam-1375	272	26	0.5	0.5	NUM
ejpam-1375	272	27	,	,	PUNCT
ejpam-1375	272	28	probability	probability	NOUN
ejpam-1375	272	29	of	of	ADP
ejpam-1375	272	30	mutation	mutation	NOUN
ejpam-1375	272	31	is	be	AUX
ejpam-1375	272	32	0.1	0.1	NUM
ejpam-1375	272	33	,	,	PUNCT
ejpam-1375	272	34	and	and	CCONJ
ejpam-1375	272	35	elitist	elitist	ADJ
ejpam-1375	272	36	rule	rule	NOUN
ejpam-1375	272	37	is	be	AUX
ejpam-1375	272	38	used	use	VERB
ejpam-1375	272	39	for	for	ADP
ejpam-1375	272	40	optimization	optimization	NOUN
ejpam-1375	272	41	.	.	PUNCT
ejpam-1375	273	1	to	to	PART
ejpam-1375	273	2	carry	carry	VERB
ejpam-1375	273	3	out	out	ADP
ejpam-1375	273	4	a	a	DET
ejpam-1375	273	5	subset	subset	ADJ
ejpam-1375	273	6	selection	selection	NOUN
ejpam-1375	273	7	of	of	ADP
ejpam-1375	273	8	variables	variable	NOUN
ejpam-1375	273	9	,	,	PUNCT
ejpam-1375	273	10	we	we	PRON
ejpam-1375	273	11	consider	consider	VERB
ejpam-1375	273	12	the	the	DET
ejpam-1375	273	13	following	follow	VERB
ejpam-1375	273	14	monte	monte	PROPN
ejpam-1375	273	15	carlo	carlo	PROPN
ejpam-1375	273	16	simulation	simulation	PROPN
ejpam-1375	273	17	protocol	protocol	PROPN
ejpam-1375	273	18	.	.	PUNCT
ejpam-1375	274	1	we	we	PRON
ejpam-1375	274	2	draw	draw	VERB
ejpam-1375	274	3	n	n	PRON
ejpam-1375	274	4	u	u	NOUN
ejpam-1375	274	5	(	(	PUNCT
ejpam-1375	274	6	0,1	0,1	NUM
ejpam-1375	274	7	)	)	PUNCT
ejpam-1375	274	8	random	random	ADJ
ejpam-1375	274	9	numbers	number	NOUN
ejpam-1375	274	10	and	and	CCONJ
ejpam-1375	274	11	include	include	VERB
ejpam-1375	274	12	a	a	DET
ejpam-1375	274	13	model	model	NOUN
ejpam-1375	274	14	in	in	ADP
ejpam-1375	274	15	the	the	DET
ejpam-1375	274	16	mating	mating	NOUN
ejpam-1375	274	17	pool	pool	NOUN
ejpam-1375	274	18	each	each	DET
ejpam-1375	274	19	time	time	NOUN
ejpam-1375	274	20	when	when	SCONJ
ejpam-1375	274	21	one	one	NUM
ejpam-1375	274	22	of	of	ADP
ejpam-1375	274	23	the	the	DET
ejpam-1375	274	24	random	random	ADJ
ejpam-1375	274	25	numbers	number	NOUN
ejpam-1375	274	26	falls	fall	VERB
ejpam-1375	274	27	within	within	ADP
ejpam-1375	274	28	its	its	PRON
ejpam-1375	274	29	bin	bin	NOUN
ejpam-1375	274	30	.	.	PUNCT
ejpam-1375	275	1	since	since	SCONJ
ejpam-1375	275	2	better	well	ADJ
ejpam-1375	275	3	models	model	NOUN
ejpam-1375	275	4	have	have	VERB
ejpam-1375	275	5	wider	wide	ADJ
ejpam-1375	275	6	bins	bin	NOUN
ejpam-1375	275	7	,	,	PUNCT
ejpam-1375	275	8	we	we	PRON
ejpam-1375	275	9	expect	expect	VERB
ejpam-1375	275	10	members	member	NOUN
ejpam-1375	275	11	of	of	ADP
ejpam-1375	275	12	the	the	DET
ejpam-1375	275	13	current	current	ADJ
ejpam-1375	275	14	generation	generation	NOUN
ejpam-1375	275	15	with	with	ADP
ejpam-1375	275	16	better	well	ADJ
ejpam-1375	275	17	model	model	NOUN
ejpam-1375	275	18	selection	selection	NOUN
ejpam-1375	275	19	criteria	criterion	NOUN
ejpam-1375	275	20	scores	score	NOUN
ejpam-1375	275	21	to	to	PART
ejpam-1375	275	22	be	be	AUX
ejpam-1375	275	23	over	over	ADV
ejpam-1375	275	24	-	-	PUNCT
ejpam-1375	275	25	represented	represent	VERB
ejpam-1375	275	26	in	in	ADP
ejpam-1375	275	27	the	the	DET
ejpam-1375	275	28	mating	mating	NOUN
ejpam-1375	275	29	pool	pool	NOUN
ejpam-1375	275	30	.	.	PUNCT
ejpam-1375	276	1	this	this	PRON
ejpam-1375	276	2	fulfills	fulfill	VERB
ejpam-1375	276	3	the	the	DET
ejpam-1375	276	4	natural	natural	ADJ
ejpam-1375	276	5	selection	selection	NOUN
ejpam-1375	276	6	role	role	NOUN
ejpam-1375	276	7	of	of	ADP
ejpam-1375	276	8	the	the	DET
ejpam-1375	276	9	ga	ga	PROPN
ejpam-1375	276	10	.	.	PUNCT
ejpam-1375	277	1	the	the	DET
ejpam-1375	277	2	mating	mate	VERB
ejpam-1375	277	3	pool	pool	NOUN
ejpam-1375	277	4	determined	determine	VERB
ejpam-1375	277	5	in	in	ADP
ejpam-1375	277	6	this	this	DET
ejpam-1375	277	7	way	way	NOUN
ejpam-1375	277	8	is	be	AUX
ejpam-1375	277	9	subjected	subject	VERB
ejpam-1375	277	10	to	to	ADP
ejpam-1375	277	11	a	a	DET
ejpam-1375	277	12	crossover	crossover	NOUN
ejpam-1375	277	13	process	process	NOUN
ejpam-1375	277	14	that	that	DET
ejpam-1375	277	15	detero	detero	PROPN
ejpam-1375	277	16	.	.	PUNCT
ejpam-1375	278	1	akbilgic	akbilgic	PROPN
ejpam-1375	278	2	,	,	PUNCT
ejpam-1375	278	3	h.	h.	PROPN
ejpam-1375	278	4	bozdogan	bozdogan	PROPN
ejpam-1375	278	5	/	/	SYM
ejpam-1375	278	6	eur	eur	PROPN
ejpam-1375	278	7	.	.	PUNCT
ejpam-1375	279	1	j.	j.	PROPN
ejpam-1375	279	2	pure	pure	PROPN
ejpam-1375	279	3	appl	appl	PROPN
ejpam-1375	279	4	.	.	PROPN
ejpam-1375	279	5	math	math	PROPN
ejpam-1375	279	6	,	,	PUNCT
ejpam-1375	279	7	4	4	NUM
ejpam-1375	279	8	(	(	PUNCT
ejpam-1375	279	9	2011	2011	NUM
ejpam-1375	279	10	)	)	PUNCT
ejpam-1375	279	11	,	,	PUNCT
ejpam-1375	279	12	467	467	NUM
ejpam-1375	279	13	-	-	SYM
ejpam-1375	279	14	485	485	NUM
ejpam-1375	279	15	479	479	NUM
ejpam-1375	279	16	mines	mine	NOUN
ejpam-1375	279	17	the	the	DET
ejpam-1375	279	18	subset	subset	NOUN
ejpam-1375	279	19	regression	regression	NOUN
ejpam-1375	279	20	models	model	NOUN
ejpam-1375	279	21	included	include	VERB
ejpam-1375	279	22	in	in	ADP
ejpam-1375	279	23	the	the	DET
ejpam-1375	279	24	next	next	ADJ
ejpam-1375	279	25	generation	generation	NOUN
ejpam-1375	279	26	.	.	PUNCT
ejpam-1375	280	1	we	we	PRON
ejpam-1375	280	2	generate	generate	VERB
ejpam-1375	280	3	7	7	NUM
ejpam-1375	280	4	predictor	predictor	NOUN
ejpam-1375	280	5	variables	variable	NOUN
ejpam-1375	280	6	by	by	ADP
ejpam-1375	280	7	using	use	VERB
ejpam-1375	280	8	a	a	DET
ejpam-1375	280	9	constant	constant	ADJ
ejpam-1375	280	10	multiple	multiple	NOUN
ejpam-1375	280	11	of	of	ADP
ejpam-1375	280	12	uniform	uniform	ADJ
ejpam-1375	280	13	random	random	ADJ
ejpam-1375	280	14	variables	variable	NOUN
ejpam-1375	280	15	between	between	ADP
ejpam-1375	280	16	0	0	NUM
ejpam-1375	280	17	and	and	CCONJ
ejpam-1375	280	18	1.that	1.that	NUM
ejpam-1375	280	19	is	be	AUX
ejpam-1375	280	20	:	:	PUNCT
ejpam-1375	280	21	x1	x1	PROPN
ejpam-1375	280	22	=	=	SYM
ejpam-1375	280	23	1×	1×	NUM
ejpam-1375	280	24	u	u	NOUN
ejpam-1375	280	25	(	(	PUNCT
ejpam-1375	280	26	0,1	0,1	NUM
ejpam-1375	280	27	)	)	PUNCT
ejpam-1375	280	28	x2	x2	NOUN
ejpam-1375	281	1	=	=	PUNCT
ejpam-1375	281	2	2×	2×	NUM
ejpam-1375	281	3	u	u	NOUN
ejpam-1375	281	4	(	(	PUNCT
ejpam-1375	281	5	0,1	0,1	NUM
ejpam-1375	281	6	)	)	PUNCT
ejpam-1375	281	7	x3	x3	NOUN
ejpam-1375	281	8	=	=	SYM
ejpam-1375	282	1	3×	3×	NUM
ejpam-1375	282	2	u	u	NOUN
ejpam-1375	282	3	(	(	PUNCT
ejpam-1375	282	4	0,1	0,1	NUM
ejpam-1375	282	5	)	)	PUNCT
ejpam-1375	282	6	x4	x4	NOUN
ejpam-1375	282	7	=	=	SYM
ejpam-1375	282	8	4×	4×	NOUN
ejpam-1375	282	9	u	u	NOUN
ejpam-1375	282	10	(	(	PUNCT
ejpam-1375	282	11	0,1	0,1	NUM
ejpam-1375	282	12	)	)	PUNCT
ejpam-1375	282	13	x5	x5	NOUN
ejpam-1375	282	14	=	=	SYM
ejpam-1375	282	15	5×	5×	PROPN
ejpam-1375	282	16	u	u	NOUN
ejpam-1375	282	17	(	(	PUNCT
ejpam-1375	282	18	0,1	0,1	NUM
ejpam-1375	282	19	)	)	PUNCT
ejpam-1375	282	20	x6	x6	NOUN
ejpam-1375	282	21	=	=	SYM
ejpam-1375	282	22	6×	6×	NUM
ejpam-1375	282	23	u	u	NOUN
ejpam-1375	282	24	(	(	PUNCT
ejpam-1375	282	25	0,1	0,1	NOUN
ejpam-1375	282	26	)	)	PUNCT
ejpam-1375	282	27	x7	x7	NOUN
ejpam-1375	282	28	=	=	SYM
ejpam-1375	283	1	7×	7×	NUM
ejpam-1375	283	2	u	u	NOUN
ejpam-1375	283	3	(	(	PUNCT
ejpam-1375	283	4	0,1	0,1	NUM
ejpam-1375	283	5	)	)	PUNCT
ejpam-1375	283	6	by	by	ADP
ejpam-1375	283	7	using	use	VERB
ejpam-1375	283	8	some	some	DET
ejpam-1375	283	9	predictors	predictor	NOUN
ejpam-1375	283	10	of	of	ADP
ejpam-1375	283	11	the	the	DET
ejpam-1375	283	12	model	model	NOUN
ejpam-1375	283	13	data	data	NOUN
ejpam-1375	283	14	matrix	matrix	NOUN
ejpam-1375	283	15	x	x	PUNCT
ejpam-1375	283	16	=	=	PUNCT
ejpam-1375	283	17	�	�	PROPN
ejpam-1375	283	18	x1	x1	PROPN
ejpam-1375	283	19	,	,	PUNCT
ejpam-1375	283	20	x2	x2	PROPN
ejpam-1375	283	21	,	,	PUNCT
ejpam-1375	283	22	x3	x3	PROPN
ejpam-1375	283	23	,	,	PUNCT
ejpam-1375	283	24	x4	x4	PROPN
ejpam-1375	283	25	,	,	PUNCT
ejpam-1375	283	26	x5	x5	PROPN
ejpam-1375	283	27	,	,	PUNCT
ejpam-1375	283	28	x6	x6	PROPN
ejpam-1375	283	29	,	,	PUNCT
ejpam-1375	283	30	x7	x7	NOUN
ejpam-1375	283	31	�	�	PROPN
ejpam-1375	283	32	,	,	PUNCT
ejpam-1375	283	33	the	the	DET
ejpam-1375	283	34	output	output	NOUN
ejpam-1375	283	35	or	or	CCONJ
ejpam-1375	283	36	the	the	DET
ejpam-1375	283	37	response	response	NOUN
ejpam-1375	283	38	variable	variable	NOUN
ejpam-1375	283	39	is	be	AUX
ejpam-1375	283	40	generated	generate	VERB
ejpam-1375	283	41	using	use	VERB
ejpam-1375	283	42	following	follow	VERB
ejpam-1375	283	43	functional	functional	ADJ
ejpam-1375	283	44	relationship	relationship	NOUN
ejpam-1375	283	45	:	:	PUNCT
ejpam-1375	283	46	y	y	PROPN
ejpam-1375	283	47	=	=	SYM
ejpam-1375	283	48	10sin	10sin	PROPN
ejpam-1375	283	49	�	�	PROPN
ejpam-1375	283	50	πx1	πx1	ADP
ejpam-1375	283	51	x2	x2	PROPN
ejpam-1375	283	52	�	�	PROPN
ejpam-1375	283	53	+	+	CCONJ
ejpam-1375	283	54	20	20	NUM
ejpam-1375	283	55	�	�	NOUN
ejpam-1375	283	56	x3	x3	NOUN
ejpam-1375	283	57	−	−	PROPN
ejpam-1375	283	58	0.5	0.5	NUM
ejpam-1375	283	59	�	�	NOUN
ejpam-1375	283	60	2	2	NUM
ejpam-1375	283	61	+	+	NOUN
ejpam-1375	283	62	10x4	10x4	NUM
ejpam-1375	283	63	+	+	SYM
ejpam-1375	283	64	ǫ	ǫ	PROPN
ejpam-1375	283	65	(	(	PUNCT
ejpam-1375	283	66	43	43	NUM
ejpam-1375	283	67	)	)	PUNCT
ejpam-1375	283	68	where	where	SCONJ
ejpam-1375	283	69	ǫ	ǫ	PRON
ejpam-1375	283	70	∼	∼	NOUN
ejpam-1375	283	71	n	n	CCONJ
ejpam-1375	283	72	(	(	PUNCT
ejpam-1375	283	73	0,1	0,1	NUM
ejpam-1375	283	74	)	)	PUNCT
ejpam-1375	283	75	.	.	PUNCT
ejpam-1375	284	1	note	note	VERB
ejpam-1375	284	2	that	that	SCONJ
ejpam-1375	284	3	in	in	ADP
ejpam-1375	284	4	this	this	DET
ejpam-1375	284	5	simulation	simulation	NOUN
ejpam-1375	284	6	protocol	protocol	NOUN
ejpam-1375	284	7	the	the	DET
ejpam-1375	284	8	first	first	ADJ
ejpam-1375	284	9	three	three	NUM
ejpam-1375	284	10	variables	variable	NOUN
ejpam-1375	284	11	are	be	AUX
ejpam-1375	284	12	nonlinear	nonlinear	ADJ
ejpam-1375	284	13	,	,	PUNCT
ejpam-1375	284	14	the	the	DET
ejpam-1375	284	15	next	next	ADJ
ejpam-1375	284	16	is	be	AUX
ejpam-1375	284	17	linear	linear	ADJ
ejpam-1375	284	18	to	to	PART
ejpam-1375	284	19	output	output	VERB
ejpam-1375	284	20	,	,	PUNCT
ejpam-1375	284	21	then	then	ADV
ejpam-1375	284	22	last	last	ADJ
ejpam-1375	284	23	3	3	NUM
ejpam-1375	284	24	variables	variable	NOUN
ejpam-1375	284	25	have	have	VERB
ejpam-1375	284	26	no	no	DET
ejpam-1375	284	27	effect	effect	NOUN
ejpam-1375	284	28	on	on	ADP
ejpam-1375	284	29	the	the	DET
ejpam-1375	284	30	response	response	NOUN
ejpam-1375	284	31	y.	y.	PROPN
ejpam-1375	284	32	therefore	therefore	ADV
ejpam-1375	284	33	,	,	PUNCT
ejpam-1375	284	34	true	true	ADJ
ejpam-1375	284	35	model	model	NOUN
ejpam-1375	284	36	includes	include	VERB
ejpam-1375	284	37	the	the	DET
ejpam-1375	284	38	regressors	regressor	NOUN
ejpam-1375	284	39	x1,x2	x1,x2	PROPN
ejpam-1375	284	40	,	,	PUNCT
ejpam-1375	284	41	x3	x3	VERB
ejpam-1375	284	42	and	and	CCONJ
ejpam-1375	284	43	,	,	PUNCT
ejpam-1375	284	44	x4	x4	PROPN
ejpam-1375	284	45	.	.	PROPN
ejpam-1375	285	1	8.1	8.1	NUM
ejpam-1375	285	2	.	.	PUNCT
ejpam-1375	286	1	simulation	simulation	NOUN
ejpam-1375	286	2	study	study	NOUN
ejpam-1375	286	3	1	1	NUM
ejpam-1375	286	4	in	in	ADP
ejpam-1375	286	5	the	the	DET
ejpam-1375	286	6	first	first	ADJ
ejpam-1375	286	7	phase	phase	NOUN
ejpam-1375	286	8	of	of	ADP
ejpam-1375	286	9	the	the	DET
ejpam-1375	286	10	simulation	simulation	NOUN
ejpam-1375	286	11	study	study	NOUN
ejpam-1375	286	12	,	,	PUNCT
ejpam-1375	286	13	we	we	PRON
ejpam-1375	286	14	choose	choose	VERB
ejpam-1375	286	15	the	the	DET
ejpam-1375	286	16	best	good	ADJ
ejpam-1375	286	17	kernel	kernel	NOUN
ejpam-1375	286	18	function	function	NOUN
ejpam-1375	286	19	for	for	ADP
ejpam-1375	286	20	hybrid	hybrid	ADJ
ejpam-1375	286	21	rbf	rbf	PROPN
ejpam-1375	286	22	model	model	NOUN
ejpam-1375	286	23	transfer	transfer	NOUN
ejpam-1375	286	24	functions	function	NOUN
ejpam-1375	286	25	according	accord	VERB
ejpam-1375	286	26	to	to	ADP
ejpam-1375	286	27	their	their	PRON
ejpam-1375	286	28	model	model	NOUN
ejpam-1375	286	29	selection	selection	NOUN
ejpam-1375	286	30	performance	performance	NOUN
ejpam-1375	286	31	.	.	PUNCT
ejpam-1375	287	1	to	to	PART
ejpam-1375	287	2	realize	realize	VERB
ejpam-1375	287	3	this	this	DET
ejpam-1375	287	4	objective	objective	NOUN
ejpam-1375	287	5	,	,	PUNCT
ejpam-1375	287	6	we	we	PRON
ejpam-1375	287	7	run	run	VERB
ejpam-1375	287	8	100	100	NUM
ejpam-1375	287	9	simulations	simulation	NOUN
ejpam-1375	287	10	using	use	VERB
ejpam-1375	287	11	different	different	ADJ
ejpam-1375	287	12	sample	sample	NOUN
ejpam-1375	287	13	sizes	size	NOUN
ejpam-1375	287	14	,	,	PUNCT
ejpam-1375	287	15	n	n	PROPN
ejpam-1375	287	16	=	=	SYM
ejpam-1375	287	17	50,100,250	50,100,250	NUM
ejpam-1375	287	18	and	and	CCONJ
ejpam-1375	287	19	500	500	NUM
ejpam-1375	287	20	,	,	PUNCT
ejpam-1375	287	21	respectively	respectively	ADV
ejpam-1375	287	22	,	,	PUNCT
ejpam-1375	287	23	and	and	CCONJ
ejpam-1375	287	24	then	then	ADV
ejpam-1375	287	25	construct	construct	VERB
ejpam-1375	287	26	hybrid	hybrid	ADJ
ejpam-1375	287	27	rbf	rbf	PROPN
ejpam-1375	287	28	model	model	NOUN
ejpam-1375	287	29	with	with	ADP
ejpam-1375	287	30	different	different	ADJ
ejpam-1375	287	31	kernel	kernel	NOUN
ejpam-1375	287	32	functions	function	NOUN
ejpam-1375	287	33	including	include	VERB
ejpam-1375	287	34	gaussian	gaussian	NOUN
ejpam-1375	287	35	,	,	PUNCT
ejpam-1375	287	36	cauchy	cauchy	PROPN
ejpam-1375	287	37	,	,	PUNCT
ejpam-1375	287	38	multiquadratic	multiquadratic	ADJ
ejpam-1375	287	39	,	,	PUNCT
ejpam-1375	287	40	and	and	CCONJ
ejpam-1375	287	41	inverse	inverse	PROPN
ejpam-1375	287	42	multiquadratic	multiquadratic	NOUN
ejpam-1375	287	43	.	.	PUNCT
ejpam-1375	288	1	the	the	DET
ejpam-1375	288	2	true	true	ADJ
ejpam-1375	288	3	model	model	NOUN
ejpam-1375	288	4	selection	selection	NOUN
ejpam-1375	288	5	percentages	percentage	NOUN
ejpam-1375	288	6	,	,	PUNCT
ejpam-1375	288	7	according	accord	VERB
ejpam-1375	288	8	to	to	ADP
ejpam-1375	288	9	icom	icom	PROPN
ejpam-1375	288	10	p(i	p(i	PROPN
ejpam-1375	288	11	f	f	PROPN
ejpam-1375	288	12	im)misspec	im)misspec	PROPN
ejpam-1375	288	13	criterion	criterion	NOUN
ejpam-1375	288	14	,	,	PUNCT
ejpam-1375	288	15	are	be	AUX
ejpam-1375	288	16	summarized	summarize	VERB
ejpam-1375	288	17	in	in	ADP
ejpam-1375	288	18	the	the	DET
ejpam-1375	288	19	table	table	NOUN
ejpam-1375	288	20	1	1	NUM
ejpam-1375	288	21	.	.	PUNCT
ejpam-1375	288	22	looking	look	VERB
ejpam-1375	288	23	at	at	ADP
ejpam-1375	288	24	the	the	DET
ejpam-1375	288	25	results	result	NOUN
ejpam-1375	288	26	in	in	ADP
ejpam-1375	288	27	table	table	NOUN
ejpam-1375	288	28	1	1	NUM
ejpam-1375	288	29	,	,	PUNCT
ejpam-1375	288	30	we	we	PRON
ejpam-1375	288	31	see	see	VERB
ejpam-1375	288	32	that	that	SCONJ
ejpam-1375	288	33	gaussian	gaussian	ADJ
ejpam-1375	288	34	kernel	kernel	NOUN
ejpam-1375	288	35	function	function	NOUN
ejpam-1375	288	36	performs	perform	VERB
ejpam-1375	288	37	the	the	DET
ejpam-1375	288	38	best	good	ADJ
ejpam-1375	288	39	astable	astable	ADJ
ejpam-1375	288	40	1	1	NUM
ejpam-1375	288	41	:	:	PUNCT
ejpam-1375	288	42	comparison	comparison	NOUN
ejpam-1375	288	43	of	of	ADP
ejpam-1375	288	44	kernel	kernel	PROPN
ejpam-1375	288	45	fun	fun	PROPN
ejpam-1375	288	46	tions	tion	NOUN
ejpam-1375	288	47	'	'	PART
ejpam-1375	288	48	performan	performan	NOUN
ejpam-1375	288	49	es	es	PROPN
ejpam-1375	288	50	.	.	PROPN
ejpam-1375	288	51	kernel	kernel	PROPN
ejpam-1375	288	52	function	function	PROPN
ejpam-1375	288	53	sample	sample	NOUN
ejpam-1375	288	54	size	size	NOUN
ejpam-1375	288	55	50	50	NUM
ejpam-1375	288	56	100	100	NUM
ejpam-1375	288	57	250	250	NUM
ejpam-1375	288	58	500	500	NUM
ejpam-1375	288	59	gauss	gaus	VERB
ejpam-1375	288	60	26	26	NUM
ejpam-1375	288	61	%	%	NOUN
ejpam-1375	288	62	49	49	NUM
ejpam-1375	288	63	%	%	NOUN
ejpam-1375	288	64	71	71	NUM
ejpam-1375	288	65	%	%	NOUN
ejpam-1375	288	66	89	89	NUM
ejpam-1375	288	67	%	%	NOUN
ejpam-1375	288	68	cauchy	cauchy	NOUN
ejpam-1375	288	69	19	19	NUM
ejpam-1375	288	70	%	%	NOUN
ejpam-1375	288	71	47	47	NUM
ejpam-1375	288	72	%	%	NOUN
ejpam-1375	288	73	71	71	NUM
ejpam-1375	288	74	%	%	NOUN
ejpam-1375	288	75	74	74	NUM
ejpam-1375	288	76	%	%	NOUN
ejpam-1375	288	77	multiquadratic	multiquadratic	ADJ
ejpam-1375	288	78	13	13	NUM
ejpam-1375	288	79	%	%	NOUN
ejpam-1375	288	80	25	25	NUM
ejpam-1375	288	81	%	%	NOUN
ejpam-1375	288	82	68	68	NUM
ejpam-1375	288	83	%	%	NOUN
ejpam-1375	288	84	87	87	NUM
ejpam-1375	288	85	%	%	NOUN
ejpam-1375	288	86	inverse	inverse	NOUN
ejpam-1375	288	87	multiquadratic	multiquadratic	NOUN
ejpam-1375	288	88	17	17	NUM
ejpam-1375	288	89	%	%	NOUN
ejpam-1375	288	90	45	45	NUM
ejpam-1375	288	91	%	%	NOUN
ejpam-1375	288	92	70	70	NUM
ejpam-1375	288	93	%	%	NOUN
ejpam-1375	288	94	78	78	NUM
ejpam-1375	288	95	%	%	NOUN
ejpam-1375	288	96	compared	compare	VERB
ejpam-1375	288	97	to	to	ADP
ejpam-1375	288	98	other	other	ADJ
ejpam-1375	288	99	kernel	kernel	NOUN
ejpam-1375	288	100	functions	function	NOUN
ejpam-1375	288	101	for	for	ADP
ejpam-1375	288	102	this	this	DET
ejpam-1375	288	103	simulated	simulate	VERB
ejpam-1375	288	104	model	model	NOUN
ejpam-1375	288	105	.	.	PUNCT
ejpam-1375	289	1	8.2	8.2	NUM
ejpam-1375	289	2	.	.	PUNCT
ejpam-1375	289	3	simulation	simulation	NOUN
ejpam-1375	289	4	study	study	NOUN
ejpam-1375	289	5	2	2	NUM
ejpam-1375	289	6	the	the	DET
ejpam-1375	289	7	second	second	ADJ
ejpam-1375	289	8	phase	phase	NOUN
ejpam-1375	289	9	of	of	ADP
ejpam-1375	289	10	the	the	DET
ejpam-1375	289	11	simulation	simulation	NOUN
ejpam-1375	289	12	study	study	NOUN
ejpam-1375	289	13	is	be	AUX
ejpam-1375	289	14	to	to	PART
ejpam-1375	289	15	compare	compare	VERB
ejpam-1375	289	16	the	the	DET
ejpam-1375	289	17	performance	performance	NOUN
ejpam-1375	289	18	of	of	ADP
ejpam-1375	289	19	the	the	DET
ejpam-1375	289	20	hybrid	hybrid	ADJ
ejpam-1375	289	21	rbf	rbf	PROPN
ejpam-1375	289	22	model	model	NOUN
ejpam-1375	289	23	approach	approach	NOUN
ejpam-1375	289	24	with	with	ADP
ejpam-1375	289	25	that	that	PRON
ejpam-1375	289	26	of	of	ADP
ejpam-1375	289	27	the	the	DET
ejpam-1375	289	28	classical	classical	ADJ
ejpam-1375	289	29	linear	linear	PROPN
ejpam-1375	289	30	regression	regression	NOUN
ejpam-1375	289	31	model	model	NOUN
ejpam-1375	289	32	.	.	PUNCT
ejpam-1375	290	1	our	our	PRON
ejpam-1375	290	2	simulation	simulation	NOUN
ejpam-1375	290	3	set	set	VERB
ejpam-1375	290	4	o.	o.	PROPN
ejpam-1375	290	5	akbilgic	akbilgic	PROPN
ejpam-1375	290	6	,	,	PUNCT
ejpam-1375	290	7	h.	h.	PROPN
ejpam-1375	290	8	bozdogan	bozdogan	PROPN
ejpam-1375	290	9	/	/	SYM
ejpam-1375	290	10	eur	eur	PROPN
ejpam-1375	290	11	.	.	PUNCT
ejpam-1375	291	1	j.	j.	PROPN
ejpam-1375	291	2	pure	pure	PROPN
ejpam-1375	291	3	appl	appl	PROPN
ejpam-1375	291	4	.	.	PROPN
ejpam-1375	291	5	math	math	PROPN
ejpam-1375	291	6	,	,	PUNCT
ejpam-1375	291	7	4	4	NUM
ejpam-1375	291	8	(	(	PUNCT
ejpam-1375	291	9	2011	2011	NUM
ejpam-1375	291	10	)	)	PUNCT
ejpam-1375	291	11	,	,	PUNCT
ejpam-1375	291	12	467	467	NUM
ejpam-1375	291	13	-	-	SYM
ejpam-1375	291	14	485	485	NUM
ejpam-1375	291	15	480	480	NUM
ejpam-1375	291	16	up	up	ADV
ejpam-1375	291	17	is	be	AUX
ejpam-1375	291	18	the	the	DET
ejpam-1375	291	19	same	same	ADJ
ejpam-1375	291	20	as	as	ADP
ejpam-1375	291	21	before	before	ADV
ejpam-1375	291	22	.	.	PUNCT
ejpam-1375	292	1	we	we	PRON
ejpam-1375	292	2	run	run	VERB
ejpam-1375	292	3	the	the	DET
ejpam-1375	292	4	simulation	simulation	NOUN
ejpam-1375	292	5	100	100	NUM
ejpam-1375	292	6	times	time	NOUN
ejpam-1375	292	7	with	with	ADP
ejpam-1375	292	8	different	different	ADJ
ejpam-1375	292	9	sample	sample	NOUN
ejpam-1375	292	10	sizes	size	NOUN
ejpam-1375	292	11	:	:	PUNCT
ejpam-1375	292	12	n	n	PROPN
ejpam-1375	292	13	=	=	SYM
ejpam-1375	292	14	50,100,250	50,100,250	NUM
ejpam-1375	292	15	and	and	CCONJ
ejpam-1375	292	16	500	500	NUM
ejpam-1375	292	17	,	,	PUNCT
ejpam-1375	292	18	respectively	respectively	ADV
ejpam-1375	292	19	and	and	CCONJ
ejpam-1375	292	20	score	score	VERB
ejpam-1375	292	21	different	different	ADJ
ejpam-1375	292	22	model	model	NOUN
ejpam-1375	292	23	selection	selection	NOUN
ejpam-1375	292	24	criteria	criterion	NOUN
ejpam-1375	292	25	aic	aic	PROPN
ejpam-1375	292	26	,	,	PUNCT
ejpam-1375	292	27	bic	bic	PROPN
ejpam-1375	292	28	,	,	PUNCT
ejpam-1375	292	29	caic	caic	PROPN
ejpam-1375	292	30	f	f	PROPN
ejpam-1375	292	31	,	,	PUNCT
ejpam-1375	292	32	icom(i	icom(i	PRON
ejpam-1375	292	33	f	f	PROPN
ejpam-1375	292	34	im)misspec	im)misspec	PROPN
ejpam-1375	292	35	under	under	ADP
ejpam-1375	292	36	the	the	DET
ejpam-1375	292	37	proposed	propose	VERB
ejpam-1375	292	38	hybrid	hybrid	NOUN
ejpam-1375	292	39	rbf	rbf	PROPN
ejpam-1375	292	40	and	and	CCONJ
ejpam-1375	292	41	the	the	DET
ejpam-1375	292	42	classic	classic	ADJ
ejpam-1375	292	43	linear	linear	PROPN
ejpam-1375	292	44	regression	regression	NOUN
ejpam-1375	292	45	model	model	NOUN
ejpam-1375	292	46	.	.	PUNCT
ejpam-1375	293	1	table	table	NOUN
ejpam-1375	293	2	2	2	NUM
ejpam-1375	293	3	summarizes	summarize	NOUN
ejpam-1375	293	4	the	the	DET
ejpam-1375	293	5	percent	percent	NOUN
ejpam-1375	293	6	hit	hit	VERB
ejpam-1375	293	7	ratios	ratio	NOUN
ejpam-1375	293	8	of	of	ADP
ejpam-1375	293	9	the	the	DET
ejpam-1375	293	10	true	true	ADJ
ejpam-1375	293	11	model.table	model.table	ADJ
ejpam-1375	293	12	2	2	NUM
ejpam-1375	293	13	:	:	PUNCT
ejpam-1375	293	14	comparison	comparison	NOUN
ejpam-1375	293	15	of	of	ADP
ejpam-1375	293	16	proposed	propose	VERB
ejpam-1375	293	17	model	model	NOUN
ejpam-1375	293	18	and	and	CCONJ
ejpam-1375	293	19	linear	linear	PROPN
ejpam-1375	293	20	regression	regression	NOUN
ejpam-1375	293	21	model	model	NOUN
ejpam-1375	293	22	.	.	PUNCT
ejpam-1375	294	1	hybrid	hybrid	PROPN
ejpam-1375	294	2	rbf	rbf	PROPN
ejpam-1375	294	3	model	model	PROPN
ejpam-1375	294	4	linear	linear	PROPN
ejpam-1375	294	5	regression	regression	PROPN
ejpam-1375	294	6	model	model	NOUN
ejpam-1375	294	7	n	n	PROPN
ejpam-1375	294	8	50	50	NUM
ejpam-1375	294	9	100	100	NUM
ejpam-1375	294	10	250	250	NUM
ejpam-1375	294	11	500	500	NUM
ejpam-1375	294	12	50	50	NUM
ejpam-1375	294	13	100	100	NUM
ejpam-1375	294	14	250	250	NUM
ejpam-1375	294	15	500	500	NUM
ejpam-1375	294	16	aic	aic	PROPN
ejpam-1375	294	17	17	17	NUM
ejpam-1375	294	18	58	58	NUM
ejpam-1375	294	19	78	78	NUM
ejpam-1375	294	20	87	87	NUM
ejpam-1375	294	21	10	10	NUM
ejpam-1375	294	22	12	12	NUM
ejpam-1375	294	23	3	3	NUM
ejpam-1375	294	24	0	0	NUM
ejpam-1375	294	25	sbc	sbc	NOUN
ejpam-1375	294	26	24	24	NUM
ejpam-1375	294	27	64	64	NUM
ejpam-1375	294	28	80	80	NUM
ejpam-1375	294	29	90	90	NUM
ejpam-1375	294	30	6	6	NUM
ejpam-1375	294	31	14	14	NUM
ejpam-1375	294	32	17	17	NUM
ejpam-1375	294	33	7	7	NUM
ejpam-1375	294	34	caicf	caicf	NOUN
ejpam-1375	294	35	19	19	NUM
ejpam-1375	294	36	50	50	NUM
ejpam-1375	294	37	84	84	NUM
ejpam-1375	294	38	87	87	NUM
ejpam-1375	294	39	14	14	NUM
ejpam-1375	294	40	24	24	NUM
ejpam-1375	294	41	45	45	NUM
ejpam-1375	294	42	24	24	NUM
ejpam-1375	294	43	icom	icom	PROPN
ejpam-1375	294	44	p(i	p(i	PROPN
ejpam-1375	294	45	f	f	PROPN
ejpam-1375	294	46	im)misspe	im)misspe	VERB
ejpam-1375	294	47	26	26	NUM
ejpam-1375	294	48	49	49	NUM
ejpam-1375	294	49	71	71	NUM
ejpam-1375	294	50	89	89	NUM
ejpam-1375	294	51	22	22	NUM
ejpam-1375	294	52	33	33	NUM
ejpam-1375	294	53	13	13	NUM
ejpam-1375	294	54	1	1	NUM
ejpam-1375	294	55	it	it	PRON
ejpam-1375	294	56	is	be	AUX
ejpam-1375	294	57	clear	clear	ADJ
ejpam-1375	294	58	from	from	ADP
ejpam-1375	294	59	table	table	NOUN
ejpam-1375	294	60	2	2	NUM
ejpam-1375	294	61	that	that	SCONJ
ejpam-1375	294	62	hybrid	hybrid	ADJ
ejpam-1375	294	63	rbf	rbf	PROPN
ejpam-1375	294	64	model	model	NOUN
ejpam-1375	294	65	is	be	AUX
ejpam-1375	294	66	superior	superior	ADJ
ejpam-1375	294	67	to	to	ADP
ejpam-1375	294	68	the	the	DET
ejpam-1375	294	69	linear	linear	ADJ
ejpam-1375	294	70	regression	regression	NOUN
ejpam-1375	294	71	model	model	NOUN
ejpam-1375	294	72	in	in	ADP
ejpam-1375	294	73	terms	term	NOUN
ejpam-1375	294	74	of	of	ADP
ejpam-1375	294	75	model	model	NOUN
ejpam-1375	294	76	selection	selection	NOUN
ejpam-1375	294	77	results	result	NOUN
ejpam-1375	294	78	.	.	PUNCT
ejpam-1375	295	1	hybrid	hybrid	PROPN
ejpam-1375	295	2	rbf	rbf	PROPN
ejpam-1375	295	3	model	model	NOUN
ejpam-1375	295	4	selects	select	VERB
ejpam-1375	295	5	the	the	DET
ejpam-1375	295	6	true	true	ADJ
ejpam-1375	295	7	model	model	NOUN
ejpam-1375	295	8	with	with	ADP
ejpam-1375	295	9	high	high	ADJ
ejpam-1375	295	10	frequency	frequency	NOUN
ejpam-1375	295	11	as	as	ADP
ejpam-1375	295	12	the	the	DET
ejpam-1375	295	13	sample	sample	NOUN
ejpam-1375	295	14	size	size	NOUN
ejpam-1375	295	15	increases	increase	NOUN
ejpam-1375	295	16	.	.	PUNCT
ejpam-1375	296	1	considering	consider	VERB
ejpam-1375	296	2	the	the	DET
ejpam-1375	296	3	highly	highly	ADV
ejpam-1375	296	4	nonlinear	nonlinear	ADJ
ejpam-1375	296	5	relationship	relationship	NOUN
ejpam-1375	296	6	between	between	ADP
ejpam-1375	296	7	input	input	NOUN
ejpam-1375	296	8	and	and	CCONJ
ejpam-1375	296	9	output	output	NOUN
ejpam-1375	296	10	variables	variable	NOUN
ejpam-1375	296	11	,	,	PUNCT
ejpam-1375	296	12	hybrid	hybrid	ADJ
ejpam-1375	296	13	rbf	rbf	PROPN
ejpam-1375	296	14	model	model	NOUN
ejpam-1375	296	15	performs	perform	VERB
ejpam-1375	296	16	better	well	ADV
ejpam-1375	296	17	in	in	ADP
ejpam-1375	296	18	terms	term	NOUN
ejpam-1375	296	19	of	of	ADP
ejpam-1375	296	20	model	model	NOUN
ejpam-1375	296	21	selection	selection	NOUN
ejpam-1375	296	22	based	base	VERB
ejpam-1375	296	23	on	on	ADP
ejpam-1375	296	24	all	all	DET
ejpam-1375	296	25	the	the	DET
ejpam-1375	296	26	information	information	NOUN
ejpam-1375	296	27	criteria	criterion	NOUN
ejpam-1375	296	28	.	.	PUNCT
ejpam-1375	297	1	the	the	DET
ejpam-1375	297	2	poor	poor	ADJ
ejpam-1375	297	3	performance	performance	NOUN
ejpam-1375	297	4	of	of	ADP
ejpam-1375	297	5	linear	linear	ADJ
ejpam-1375	297	6	regression	regression	NOUN
ejpam-1375	297	7	model	model	NOUN
ejpam-1375	297	8	on	on	ADP
ejpam-1375	297	9	a	a	DET
ejpam-1375	297	10	simulated	simulate	VERB
ejpam-1375	297	11	true	true	ADJ
ejpam-1375	297	12	freidman	freidman	PROPN
ejpam-1375	297	13	model	model	NOUN
ejpam-1375	297	14	which	which	PRON
ejpam-1375	297	15	has	have	VERB
ejpam-1375	297	16	nonlinear	nonlinear	ADJ
ejpam-1375	297	17	structure	structure	NOUN
ejpam-1375	297	18	is	be	AUX
ejpam-1375	297	19	not	not	PART
ejpam-1375	297	20	so	so	ADV
ejpam-1375	297	21	surprising	surprising	ADJ
ejpam-1375	297	22	since	since	SCONJ
ejpam-1375	297	23	the	the	DET
ejpam-1375	297	24	linear	linear	ADJ
ejpam-1375	297	25	regression	regression	NOUN
ejpam-1375	297	26	does	do	AUX
ejpam-1375	297	27	not	not	PART
ejpam-1375	297	28	take	take	VERB
ejpam-1375	297	29	model	model	NOUN
ejpam-1375	297	30	misspecification	misspecification	NOUN
ejpam-1375	297	31	into	into	ADP
ejpam-1375	297	32	account	account	NOUN
ejpam-1375	297	33	and	and	CCONJ
ejpam-1375	297	34	can	can	AUX
ejpam-1375	297	35	not	not	PART
ejpam-1375	297	36	handle	handle	VERB
ejpam-1375	297	37	the	the	DET
ejpam-1375	297	38	singularity	singularity	NOUN
ejpam-1375	297	39	problem	problem	NOUN
ejpam-1375	297	40	in	in	ADP
ejpam-1375	297	41	the	the	DET
ejpam-1375	297	42	design	design	NOUN
ejpam-1375	297	43	matrix	matrix	NOUN
ejpam-1375	297	44	,	,	PUNCT
ejpam-1375	297	45	h	h	NOUN
ejpam-1375	298	1	′	′	NUM
ejpam-1375	298	2	h.	h.	NOUN
ejpam-1375	298	3	on	on	ADP
ejpam-1375	298	4	the	the	DET
ejpam-1375	298	5	other	other	ADJ
ejpam-1375	298	6	hand	hand	NOUN
ejpam-1375	298	7	,	,	PUNCT
ejpam-1375	298	8	due	due	ADP
ejpam-1375	298	9	to	to	ADP
ejpam-1375	298	10	function	function	VERB
ejpam-1375	298	11	approximation	approximation	NOUN
ejpam-1375	298	12	and	and	CCONJ
ejpam-1375	298	13	implicit	implicit	ADJ
ejpam-1375	298	14	smoothing	smoothing	NOUN
ejpam-1375	298	15	properties	property	NOUN
ejpam-1375	298	16	of	of	ADP
ejpam-1375	298	17	the	the	DET
ejpam-1375	298	18	hybrid	hybrid	ADJ
ejpam-1375	298	19	rbf	rbf	PROPN
ejpam-1375	298	20	approach	approach	NOUN
ejpam-1375	298	21	guards	guard	VERB
ejpam-1375	298	22	us	we	PRON
ejpam-1375	298	23	from	from	ADP
ejpam-1375	298	24	model	model	NOUN
ejpam-1375	298	25	misspecification	misspecification	NOUN
ejpam-1375	298	26	as	as	SCONJ
ejpam-1375	298	27	shown	show	VERB
ejpam-1375	298	28	in	in	ADP
ejpam-1375	298	29	our	our	PRON
ejpam-1375	298	30	simulation	simulation	NOUN
ejpam-1375	298	31	results	result	NOUN
ejpam-1375	298	32	in	in	ADP
ejpam-1375	298	33	terms	term	NOUN
ejpam-1375	298	34	of	of	ADP
ejpam-1375	298	35	its	its	PRON
ejpam-1375	298	36	outstanding	outstanding	ADJ
ejpam-1375	298	37	performance	performance	NOUN
ejpam-1375	298	38	.	.	PUNCT
ejpam-1375	299	1	8.3	8.3	NUM
ejpam-1375	299	2	.	.	PUNCT
ejpam-1375	299	3	simulation	simulation	NOUN
ejpam-1375	299	4	study	study	VERB
ejpam-1375	299	5	3	3	NUM
ejpam-1375	299	6	third	third	ADJ
ejpam-1375	299	7	and	and	CCONJ
ejpam-1375	299	8	last	last	ADJ
ejpam-1375	299	9	phase	phase	NOUN
ejpam-1375	299	10	of	of	ADP
ejpam-1375	299	11	our	our	PRON
ejpam-1375	299	12	simulation	simulation	NOUN
ejpam-1375	299	13	study	study	NOUN
ejpam-1375	299	14	is	be	AUX
ejpam-1375	299	15	to	to	PART
ejpam-1375	299	16	determine	determine	VERB
ejpam-1375	299	17	the	the	DET
ejpam-1375	299	18	estimation	estimation	NOUN
ejpam-1375	299	19	and	and	CCONJ
ejpam-1375	299	20	prediction	prediction	NOUN
ejpam-1375	299	21	success	success	NOUN
ejpam-1375	299	22	of	of	ADP
ejpam-1375	299	23	hybrid	hybrid	ADJ
ejpam-1375	299	24	rbf	rbf	PROPN
ejpam-1375	299	25	model	model	NOUN
ejpam-1375	299	26	using	use	VERB
ejpam-1375	299	27	the	the	DET
ejpam-1375	299	28	same	same	ADJ
ejpam-1375	299	29	simulation	simulation	NOUN
ejpam-1375	299	30	protocol	protocol	NOUN
ejpam-1375	299	31	as	as	ADP
ejpam-1375	299	32	above	above	ADV
ejpam-1375	299	33	.	.	PUNCT
ejpam-1375	300	1	we	we	PRON
ejpam-1375	300	2	generate	generate	VERB
ejpam-1375	300	3	training	training	NOUN
ejpam-1375	300	4	data	datum	NOUN
ejpam-1375	300	5	with	with	ADP
ejpam-1375	300	6	sample	sample	NOUN
ejpam-1375	300	7	sizes	size	NOUN
ejpam-1375	300	8	:	:	PUNCT
ejpam-1375	300	9	n	n	PROPN
ejpam-1375	300	10	=	=	SYM
ejpam-1375	300	11	50,100,250	50,100,250	NUM
ejpam-1375	300	12	and	and	CCONJ
ejpam-1375	300	13	500	500	NUM
ejpam-1375	300	14	.	.	PUNCT
ejpam-1375	301	1	we	we	PRON
ejpam-1375	301	2	use	use	VERB
ejpam-1375	301	3	20	20	NUM
ejpam-1375	301	4	observations	observation	NOUN
ejpam-1375	301	5	of	of	ADP
ejpam-1375	301	6	the	the	DET
ejpam-1375	301	7	test	test	NOUN
ejpam-1375	301	8	data	datum	NOUN
ejpam-1375	301	9	for	for	ADP
ejpam-1375	301	10	each	each	PRON
ejpam-1375	301	11	.	.	PUNCT
ejpam-1375	302	1	first	first	ADV
ejpam-1375	302	2	,	,	PUNCT
ejpam-1375	302	3	we	we	PRON
ejpam-1375	302	4	learn	learn	VERB
ejpam-1375	302	5	model	model	NOUN
ejpam-1375	302	6	parameters	parameter	NOUN
ejpam-1375	302	7	from	from	ADP
ejpam-1375	302	8	test	test	NOUN
ejpam-1375	302	9	data	datum	NOUN
ejpam-1375	302	10	and	and	CCONJ
ejpam-1375	302	11	then	then	ADV
ejpam-1375	302	12	we	we	PRON
ejpam-1375	302	13	predict	predict	VERB
ejpam-1375	302	14	our	our	PRON
ejpam-1375	302	15	results	result	NOUN
ejpam-1375	302	16	from	from	ADP
ejpam-1375	302	17	the	the	DET
ejpam-1375	302	18	data	datum	NOUN
ejpam-1375	302	19	using	use	VERB
ejpam-1375	302	20	parameters	parameter	NOUN
ejpam-1375	302	21	determined	determine	VERB
ejpam-1375	302	22	from	from	ADP
ejpam-1375	302	23	the	the	DET
ejpam-1375	302	24	training	training	NOUN
ejpam-1375	302	25	data	datum	NOUN
ejpam-1375	302	26	.	.	PUNCT
ejpam-1375	303	1	table	table	NOUN
ejpam-1375	303	2	3	3	NUM
ejpam-1375	303	3	gives	give	VERB
ejpam-1375	303	4	the	the	DET
ejpam-1375	303	5	training	training	NOUN
ejpam-1375	303	6	and	and	CCONJ
ejpam-1375	303	7	testing	testing	NOUN
ejpam-1375	303	8	errors	error	NOUN
ejpam-1375	303	9	in	in	ADP
ejpam-1375	303	10	two	two	NUM
ejpam-1375	303	11	ways	way	NOUN
ejpam-1375	303	12	.	.	PUNCT
ejpam-1375	304	1	we	we	PRON
ejpam-1375	304	2	also	also	ADV
ejpam-1375	304	3	report	report	VERB
ejpam-1375	304	4	the	the	DET
ejpam-1375	304	5	root	root	NOUN
ejpam-1375	304	6	mean	mean	VERB
ejpam-1375	304	7	square	square	ADJ
ejpam-1375	304	8	error	error	NOUN
ejpam-1375	304	9	(	(	PUNCT
ejpam-1375	304	10	rmse	rmse	NOUN
ejpam-1375	304	11	)	)	PUNCT
ejpam-1375	304	12	and	and	CCONJ
ejpam-1375	304	13	root	root	NOUN
ejpam-1375	304	14	mean	mean	NOUN
ejpam-1375	304	15	square	square	ADJ
ejpam-1375	304	16	percentage	percentage	NOUN
ejpam-1375	304	17	error	error	NOUN
ejpam-1375	304	18	(	(	PUNCT
ejpam-1375	304	19	rmspe	rmspe	PROPN
ejpam-1375	304	20	)	)	PUNCT
ejpam-1375	304	21	.	.	PUNCT
ejpam-1375	305	1	figures	figure	NOUN
ejpam-1375	305	2	1	1	NUM
ejpam-1375	305	3	and	and	CCONJ
ejpam-1375	305	4	2	2	NUM
ejpam-1375	305	5	show	show	VERB
ejpam-1375	305	6	that	that	SCONJ
ejpam-1375	305	7	hybrid	hybrid	NOUN
ejpam-1375	305	8	rbf	rbf	PROPN
ejpam-1375	305	9	model	model	NOUN
ejpam-1375	305	10	fits	fit	VERB
ejpam-1375	305	11	the	the	DET
ejpam-1375	305	12	data	datum	NOUN
ejpam-1375	305	13	very	very	ADV
ejpam-1375	305	14	well	well	ADV
ejpam-1375	305	15	not	not	PART
ejpam-1375	305	16	only	only	ADV
ejpam-1375	305	17	for	for	ADP
ejpam-1375	305	18	training	training	NOUN
ejpam-1375	305	19	data	datum	NOUN
ejpam-1375	305	20	but	but	CCONJ
ejpam-1375	305	21	also	also	ADV
ejpam-1375	305	22	for	for	ADP
ejpam-1375	305	23	test	test	NOUN
ejpam-1375	305	24	data	datum	NOUN
ejpam-1375	305	25	.	.	PUNCT
ejpam-1375	306	1	this	this	DET
ejpam-1375	306	2	aspect	aspect	NOUN
ejpam-1375	306	3	can	can	AUX
ejpam-1375	306	4	be	be	AUX
ejpam-1375	306	5	an	an	DET
ejpam-1375	306	6	evidence	evidence	NOUN
ejpam-1375	306	7	to	to	PART
ejpam-1375	306	8	claim	claim	VERB
ejpam-1375	306	9	that	that	SCONJ
ejpam-1375	306	10	hybrid	hybrid	ADJ
ejpam-1375	306	11	rbf	rbf	PROPN
ejpam-1375	306	12	model	model	NOUN
ejpam-1375	306	13	learns	learn	VERB
ejpam-1375	306	14	the	the	DET
ejpam-1375	306	15	relationship	relationship	NOUN
ejpam-1375	306	16	within	within	ADP
ejpam-1375	306	17	the	the	DET
ejpam-1375	306	18	regression	regression	NOUN
ejpam-1375	306	19	data	datum	NOUN
ejpam-1375	306	20	set	set	VERB
ejpam-1375	306	21	considered	consider	VERB
ejpam-1375	306	22	.	.	PUNCT
ejpam-1375	307	1	although	although	SCONJ
ejpam-1375	307	2	the	the	DET
ejpam-1375	307	3	structure	structure	NOUN
ejpam-1375	307	4	of	of	ADP
ejpam-1375	307	5	hybrid	hybrid	ADJ
ejpam-1375	307	6	rbf	rbf	PROPN
ejpam-1375	307	7	model	model	NOUN
ejpam-1375	307	8	represents	represent	VERB
ejpam-1375	307	9	a	a	DET
ejpam-1375	307	10	very	very	ADV
ejpam-1375	307	11	complicated	complicated	ADJ
ejpam-1375	307	12	equation	equation	NOUN
ejpam-1375	307	13	,	,	PUNCT
ejpam-1375	307	14	we	we	PRON
ejpam-1375	307	15	can	can	AUX
ejpam-1375	307	16	build	build	VERB
ejpam-1375	307	17	the	the	DET
ejpam-1375	307	18	final	final	ADJ
ejpam-1375	307	19	best	good	ADJ
ejpam-1375	307	20	fitting	fitting	ADJ
ejpam-1375	307	21	rbf	rbf	PROPN
ejpam-1375	307	22	-	-	PUNCT
ejpam-1375	307	23	nn	nn	PROPN
ejpam-1375	307	24	model	model	NOUN
ejpam-1375	307	25	in	in	ADP
ejpam-1375	307	26	its	its	PRON
ejpam-1375	307	27	open	open	ADJ
ejpam-1375	307	28	analytical	analytical	ADJ
ejpam-1375	307	29	form	form	NOUN
ejpam-1375	307	30	using	use	VERB
ejpam-1375	307	31	the	the	DET
ejpam-1375	307	32	recovered	recovered	ADJ
ejpam-1375	307	33	rbf	rbf	PROPN
ejpam-1375	307	34	centers	center	NOUN
ejpam-1375	307	35	,	,	PUNCT
ejpam-1375	307	36	c	c	X
ejpam-1375	307	37	,	,	PUNCT
ejpam-1375	307	38	radius	radius	NOUN
ejpam-1375	307	39	r	r	NOUN
ejpam-1375	307	40	,	,	PUNCT
ejpam-1375	307	41	and	and	CCONJ
ejpam-1375	307	42	the	the	DET
ejpam-1375	307	43	regression	regression	NOUN
ejpam-1375	307	44	weights	weight	NOUN
ejpam-1375	307	45	,	,	PUNCT
ejpam-1375	307	46	w.	w.	PROPN
ejpam-1375	307	47	in	in	ADP
ejpam-1375	307	48	(	(	PUNCT
ejpam-1375	307	49	44	44	NUM
ejpam-1375	307	50	)	)	PUNCT
ejpam-1375	307	51	we	we	PRON
ejpam-1375	307	52	show	show	VERB
ejpam-1375	307	53	the	the	DET
ejpam-1375	307	54	constructed	construct	VERB
ejpam-1375	307	55	hybrid	hybrid	NOUN
ejpam-1375	307	56	rbf	rbf	PROPN
ejpam-1375	307	57	model	model	NOUN
ejpam-1375	307	58	obtained	obtain	VERB
ejpam-1375	307	59	for	for	ADP
ejpam-1375	307	60	sample	sample	NOUN
ejpam-1375	307	61	size	size	NOUN
ejpam-1375	307	62	n	n	NOUN
ejpam-1375	307	63	=	=	NUM
ejpam-1375	307	64	250	250	NUM
ejpam-1375	307	65	from	from	ADP
ejpam-1375	307	66	the	the	DET
ejpam-1375	307	67	the	the	DET
ejpam-1375	307	68	generated	generate	VERB
ejpam-1375	307	69	regression	regression	NOUN
ejpam-1375	307	70	tree	tree	NOUN
ejpam-1375	307	71	which	which	PRON
ejpam-1375	307	72	is	be	AUX
ejpam-1375	307	73	shown	show	VERB
ejpam-1375	307	74	in	in	ADP
ejpam-1375	307	75	figure	figure	NOUN
ejpam-1375	307	76	3	3	NUM
ejpam-1375	307	77	.	.	PUNCT
ejpam-1375	307	78	o.	o.	PROPN
ejpam-1375	307	79	akbilgic	akbilgic	PROPN
ejpam-1375	307	80	,	,	PUNCT
ejpam-1375	307	81	h.	h.	PROPN
ejpam-1375	307	82	bozdogan	bozdogan	PROPN
ejpam-1375	307	83	/	/	SYM
ejpam-1375	307	84	eur	eur	PROPN
ejpam-1375	307	85	.	.	PUNCT
ejpam-1375	308	1	j.	j.	PROPN
ejpam-1375	308	2	pure	pure	PROPN
ejpam-1375	308	3	appl	appl	PROPN
ejpam-1375	308	4	.	.	PROPN
ejpam-1375	308	5	math	math	PROPN
ejpam-1375	308	6	,	,	PUNCT
ejpam-1375	308	7	4	4	NUM
ejpam-1375	308	8	(	(	PUNCT
ejpam-1375	308	9	2011	2011	NUM
ejpam-1375	308	10	)	)	PUNCT
ejpam-1375	308	11	,	,	PUNCT
ejpam-1375	308	12	467	467	NUM
ejpam-1375	308	13	-	-	SYM
ejpam-1375	308	14	485	485	NUM
ejpam-1375	308	15	481table	481table	ADJ
ejpam-1375	308	16	3	3	NUM
ejpam-1375	308	17	:	:	PUNCT
ejpam-1375	308	18	estimation	estimation	NOUN
ejpam-1375	308	19	and	and	CCONJ
ejpam-1375	308	20	predi	predi	NOUN
ejpam-1375	308	21	tion	tion	PROPN
ejpam-1375	308	22	performan	performan	NOUN
ejpam-1375	308	23	e	e	PROPN
ejpam-1375	308	24	of	of	ADP
ejpam-1375	308	25	hybrid	hybrid	PROPN
ejpam-1375	308	26	rbf	rbf	PROPN
ejpam-1375	308	27	model	model	PROPN
ejpam-1375	308	28	.	.	PUNCT
ejpam-1375	309	1	error	error	NOUN
ejpam-1375	309	2	type	type	NOUN
ejpam-1375	309	3	sample	sample	NOUN
ejpam-1375	309	4	size	size	NOUN
ejpam-1375	309	5	rmse	rmse	PROPN
ejpam-1375	309	6	rmspe	rmspe	PROPN
ejpam-1375	309	7	train	train	NOUN
ejpam-1375	309	8	-	-	PUNCT
ejpam-1375	309	9	test	test	NOUN
ejpam-1375	309	10	train	train	NOUN
ejpam-1375	309	11	test	test	NOUN
ejpam-1375	309	12	train	train	NOUN
ejpam-1375	309	13	test	test	NOUN
ejpam-1375	309	14	50−	50−	NOUN
ejpam-1375	309	15	20	20	NUM
ejpam-1375	309	16	9.89	9.89	NUM
ejpam-1375	309	17	12.39	12.39	NUM
ejpam-1375	309	18	7.27	7.27	NUM
ejpam-1375	309	19	3.40	3.40	NUM
ejpam-1375	309	20	100−	100−	NUM
ejpam-1375	309	21	20	20	NUM
ejpam-1375	309	22	8.08	8.08	NUM
ejpam-1375	309	23	8.18	8.18	NUM
ejpam-1375	309	24	11.64	11.64	NUM
ejpam-1375	309	25	5.09	5.09	NUM
ejpam-1375	309	26	250−	250−	NUM
ejpam-1375	309	27	20	20	NUM
ejpam-1375	309	28	9.31	9.31	NUM
ejpam-1375	309	29	10.83	10.83	NUM
ejpam-1375	309	30	5.33	5.33	NUM
ejpam-1375	309	31	2.07	2.07	NUM
ejpam-1375	309	32	500−	500−	NUM
ejpam-1375	309	33	20	20	NUM
ejpam-1375	309	34	8.48	8.48	NUM
ejpam-1375	309	35	7.90	7.90	NUM
ejpam-1375	309	36	5.20	5.20	NUM
ejpam-1375	309	37	2.38	2.38	NUM
ejpam-1375	309	38	0	0	NUM
ejpam-1375	310	1	50	50	NUM
ejpam-1375	310	2	100	100	NUM
ejpam-1375	310	3	150	150	NUM
ejpam-1375	310	4	200	200	NUM
ejpam-1375	310	5	250	250	NUM
ejpam-1375	310	6	−50	−50	NOUN
ejpam-1375	310	7	0	0	NUM
ejpam-1375	310	8	50	50	NUM
ejpam-1375	310	9	100	100	NUM
ejpam-1375	310	10	150	150	NUM
ejpam-1375	310	11	200	200	NUM
ejpam-1375	310	12	number	number	NOUN
ejpam-1375	310	13	of	of	ADP
ejpam-1375	310	14	observation	observation	NOUN
ejpam-1375	310	15	y	y	PROPN
ejpam-1375	310	16	yhat	yhat	PROPN
ejpam-1375	310	17	figure	figure	NOUN
ejpam-1375	310	18	1	1	NUM
ejpam-1375	310	19	:	:	PUNCT
ejpam-1375	310	20	observed	observed	ADJ
ejpam-1375	310	21	and	and	CCONJ
ejpam-1375	310	22	estimated	estimate	VERB
ejpam-1375	310	23	values	value	NOUN
ejpam-1375	310	24	of	of	ADP
ejpam-1375	310	25	y	y	PROPN
ejpam-1375	310	26	for	for	ADP
ejpam-1375	310	27	training	training	NOUN
ejpam-1375	310	28	data	datum	NOUN
ejpam-1375	310	29	.	.	PUNCT
ejpam-1375	311	1	0	0	NUM
ejpam-1375	311	2	2	2	NUM
ejpam-1375	311	3	4	4	NUM
ejpam-1375	311	4	6	6	NUM
ejpam-1375	311	5	8	8	NUM
ejpam-1375	311	6	10	10	NUM
ejpam-1375	311	7	12	12	NUM
ejpam-1375	311	8	14	14	NUM
ejpam-1375	311	9	16	16	NUM
ejpam-1375	311	10	18	18	NUM
ejpam-1375	311	11	20	20	NUM
ejpam-1375	311	12	0	0	NUM
ejpam-1375	311	13	50	50	NUM
ejpam-1375	311	14	100	100	NUM
ejpam-1375	311	15	150	150	NUM
ejpam-1375	311	16	number	number	NOUN
ejpam-1375	311	17	of	of	ADP
ejpam-1375	311	18	observation	observation	NOUN
ejpam-1375	311	19	y	y	PROPN
ejpam-1375	311	20	yhat	yhat	PROPN
ejpam-1375	311	21	figure	figure	NOUN
ejpam-1375	311	22	2	2	NUM
ejpam-1375	311	23	:	:	PUNCT
ejpam-1375	311	24	observed	observed	ADJ
ejpam-1375	311	25	and	and	CCONJ
ejpam-1375	311	26	estimated	estimate	VERB
ejpam-1375	311	27	values	value	NOUN
ejpam-1375	311	28	of	of	ADP
ejpam-1375	311	29	y	y	PROPN
ejpam-1375	311	30	for	for	ADP
ejpam-1375	311	31	test	test	NOUN
ejpam-1375	311	32	data	datum	NOUN
ejpam-1375	311	33	.	.	PUNCT
ejpam-1375	312	1	o.	o.	PROPN
ejpam-1375	312	2	akbilgic	akbilgic	PROPN
ejpam-1375	312	3	,	,	PUNCT
ejpam-1375	312	4	h.	h.	PROPN
ejpam-1375	312	5	bozdogan	bozdogan	PROPN
ejpam-1375	312	6	/	/	SYM
ejpam-1375	312	7	eur	eur	PROPN
ejpam-1375	312	8	.	.	PUNCT
ejpam-1375	313	1	j.	j.	PROPN
ejpam-1375	313	2	pure	pure	PROPN
ejpam-1375	313	3	appl	appl	PROPN
ejpam-1375	313	4	.	.	PROPN
ejpam-1375	313	5	math	math	PROPN
ejpam-1375	313	6	,	,	PUNCT
ejpam-1375	313	7	4	4	NUM
ejpam-1375	313	8	(	(	PUNCT
ejpam-1375	313	9	2011	2011	NUM
ejpam-1375	313	10	)	)	PUNCT
ejpam-1375	313	11	,	,	PUNCT
ejpam-1375	313	12	467	467	NUM
ejpam-1375	313	13	-	-	SYM
ejpam-1375	313	14	485	485	NUM
ejpam-1375	313	15	482	482	NUM
ejpam-1375	313	16	figure	figure	NOUN
ejpam-1375	313	17	3	3	NUM
ejpam-1375	313	18	:	:	PUNCT
ejpam-1375	313	19	regression	regression	NOUN
ejpam-1375	313	20	tree	tree	NOUN
ejpam-1375	313	21	developed	develop	VERB
ejpam-1375	313	22	for	for	ADP
ejpam-1375	313	23	n	n	NOUN
ejpam-1375	313	24	=	=	NUM
ejpam-1375	313	25	250	250	NUM
ejpam-1375	313	26	.	.	PUNCT
ejpam-1375	314	1	o.	o.	PROPN
ejpam-1375	314	2	akbilgic	akbilgic	PROPN
ejpam-1375	314	3	,	,	PUNCT
ejpam-1375	314	4	h.	h.	PROPN
ejpam-1375	314	5	bozdogan	bozdogan	PROPN
ejpam-1375	314	6	/	/	SYM
ejpam-1375	314	7	eur	eur	PROPN
ejpam-1375	314	8	.	.	PUNCT
ejpam-1375	315	1	j.	j.	PROPN
ejpam-1375	315	2	pure	pure	PROPN
ejpam-1375	315	3	appl	appl	PROPN
ejpam-1375	315	4	.	.	PROPN
ejpam-1375	315	5	math	math	PROPN
ejpam-1375	315	6	,	,	PUNCT
ejpam-1375	315	7	4	4	NUM
ejpam-1375	315	8	(	(	PUNCT
ejpam-1375	315	9	2011	2011	NUM
ejpam-1375	315	10	)	)	PUNCT
ejpam-1375	315	11	,	,	PUNCT
ejpam-1375	315	12	467	467	NUM
ejpam-1375	315	13	-	-	SYM
ejpam-1375	315	14	485	485	NUM
ejpam-1375	315	15	483	483	NUM
ejpam-1375	315	16	y	y	NOUN
ejpam-1375	315	17	=	=	SYM
ejpam-1375	315	18	10sin	10sin	PROPN
ejpam-1375	315	19	�	�	PROPN
ejpam-1375	315	20	πx1	πx1	ADP
ejpam-1375	315	21	x2	x2	PROPN
ejpam-1375	315	22	�	�	PROPN
ejpam-1375	315	23	+	+	CCONJ
ejpam-1375	315	24	20	20	NUM
ejpam-1375	315	25	�	�	NOUN
ejpam-1375	315	26	x3−	x3−	PROPN
ejpam-1375	315	27	0.5	0.5	NUM
ejpam-1375	315	28	�	�	NOUN
ejpam-1375	315	29	2	2	NUM
ejpam-1375	315	30	+	+	NOUN
ejpam-1375	315	31	10x4	10x4	NUM
ejpam-1375	315	32	+	+	SYM
ejpam-1375	315	33	ǫ	ǫ	PRON
ejpam-1375	315	34	(	(	PUNCT
ejpam-1375	315	35	44	44	NUM
ejpam-1375	315	36	)	)	PUNCT
ejpam-1375	316	1	≈	≈	PROPN
ejpam-1375	316	2	483.56ex	483.56ex	NOUN
ejpam-1375	316	3	p	p	PROPN
ejpam-1375	316	4	�	�	PROPN
ejpam-1375	316	5	−	−	PROPN
ejpam-1375	316	6	�	�	PROPN
ejpam-1375	317	1	x1	x1	PROPN
ejpam-1375	317	2	−	−	PROPN
ejpam-1375	317	3	0.49	0.49	NUM
ejpam-1375	317	4	0.99	0.99	NUM
ejpam-1375	317	5	�	�	SYM
ejpam-1375	317	6	2	2	NUM
ejpam-1375	317	7	+	+	CCONJ
ejpam-1375	317	8	�	�	PROPN
ejpam-1375	317	9	x2−	x2−	PROPN
ejpam-1375	317	10	1.00	1.00	NUM
ejpam-1375	317	11	1.99	1.99	NUM
ejpam-1375	317	12	�	�	SYM
ejpam-1375	317	13	2	2	NUM
ejpam-1375	317	14	+	+	NUM
ejpam-1375	317	15	�	�	NOUN
ejpam-1375	317	16	x3−	x3−	PROPN
ejpam-1375	317	17	1.50	1.50	NUM
ejpam-1375	317	18	2.99	2.99	NUM
ejpam-1375	317	19	�	�	SYM
ejpam-1375	317	20	2	2	NUM
ejpam-1375	317	21	+	+	NUM
ejpam-1375	317	22	�	�	PROPN
ejpam-1375	317	23	x4	x4	ADP
ejpam-1375	317	24	−	−	PROPN
ejpam-1375	317	25	2.00	2.00	NUM
ejpam-1375	317	26	3.97	3.97	NUM
ejpam-1375	317	27	�	�	PROPN
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ejpam-1375	317	30	−	−	PROPN
ejpam-1375	317	31	405.34ex	405.34ex	NUM
ejpam-1375	317	32	p	p	PROPN
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ejpam-1375	317	36	x1	x1	PROPN
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ejpam-1375	318	4	�	�	SYM
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ejpam-1375	318	6	+	+	CCONJ
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ejpam-1375	318	8	x2−	x2−	PROPN
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ejpam-1375	318	31	p	p	X
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ejpam-1375	318	34	�	�	PROPN
ejpam-1375	318	35	x1−	x1−	PROPN
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ejpam-1375	318	37	0.99	0.99	NUM
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ejpam-1375	321	7	x3−	x3−	PROPN
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ejpam-1375	321	14	x4−	x4−	PROPN
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ejpam-1375	321	25	�	�	PROPN
ejpam-1375	322	1	x1−	x1−	PROPN
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ejpam-1375	323	7	+	+	NUM
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ejpam-1375	323	9	x3−	x3−	PROPN
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ejpam-1375	323	19	�	�	PROPN
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ejpam-1375	323	21	�	�	NOUN
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ejpam-1375	327	25	�	�	PROPN
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ejpam-1375	341	20	+	+	NUM
ejpam-1375	341	21	�	�	PROPN
ejpam-1375	341	22	x4	x4	PROPN
ejpam-1375	341	23	−	−	PROPN
ejpam-1375	341	24	3.06	3.06	NUM
ejpam-1375	341	25	1.84	1.84	NUM
ejpam-1375	341	26	�	�	SYM
ejpam-1375	341	27	2	2	NUM
ejpam-1375	341	28	�	�	NOUN
ejpam-1375	341	29	+	+	CCONJ
ejpam-1375	341	30	9.03ex	9.03ex	NUM
ejpam-1375	341	31	p	p	PROPN
ejpam-1375	341	32	�	�	PROPN
ejpam-1375	341	33	−	−	PROPN
ejpam-1375	341	34	�	�	PROPN
ejpam-1375	341	35	x1	x1	PROPN
ejpam-1375	342	1	−	−	PROPN
ejpam-1375	342	2	0.49	0.49	NUM
ejpam-1375	342	3	0.99	0.99	NUM
ejpam-1375	342	4	�	�	SYM
ejpam-1375	342	5	2	2	NUM
ejpam-1375	342	6	+	+	NUM
ejpam-1375	342	7	�	�	PROPN
ejpam-1375	342	8	x2−	x2−	PROPN
ejpam-1375	342	9	0.76	0.76	NUM
ejpam-1375	342	10	1.52	1.52	NUM
ejpam-1375	342	11	�	�	NOUN
ejpam-1375	342	12	2	2	NUM
ejpam-1375	342	13	+	+	NUM
ejpam-1375	342	14	�	�	NOUN
ejpam-1375	342	15	x3−	x3−	PROPN
ejpam-1375	342	16	1.20	1.20	NUM
ejpam-1375	342	17	0.47	0.47	NUM
ejpam-1375	342	18	�	�	PROPN
ejpam-1375	342	19	2	2	NUM
ejpam-1375	342	20	+	+	NUM
ejpam-1375	342	21	�	�	PROPN
ejpam-1375	342	22	x4	x4	CCONJ
ejpam-1375	342	23	−	−	PROPN
ejpam-1375	342	24	0.95	0.95	NUM
ejpam-1375	342	25	1.87	1.87	NUM
ejpam-1375	342	26	�	�	PROPN
ejpam-1375	342	27	2	2	NUM
ejpam-1375	342	28	�	�	PROPN
ejpam-1375	342	29	references	reference	NOUN
ejpam-1375	342	30	484	484	NUM
ejpam-1375	342	31	9	9	NUM
ejpam-1375	342	32	.	.	PUNCT
ejpam-1375	343	1	conclusions	conclusion	NOUN
ejpam-1375	343	2	in	in	ADP
ejpam-1375	343	3	this	this	DET
ejpam-1375	343	4	paper	paper	NOUN
ejpam-1375	343	5	,	,	PUNCT
ejpam-1375	343	6	we	we	PRON
ejpam-1375	343	7	have	have	AUX
ejpam-1375	343	8	tackled	tackle	VERB
ejpam-1375	343	9	a	a	DET
ejpam-1375	343	10	very	very	ADV
ejpam-1375	343	11	important	important	ADJ
ejpam-1375	343	12	and	and	CCONJ
ejpam-1375	343	13	common	common	ADJ
ejpam-1375	343	14	problem	problem	NOUN
ejpam-1375	343	15	in	in	ADP
ejpam-1375	343	16	statistical	statistical	ADJ
ejpam-1375	343	17	analysis	analysis	NOUN
ejpam-1375	343	18	of	of	ADP
ejpam-1375	343	19	predictive	predictive	ADJ
ejpam-1375	343	20	regression	regression	NOUN
ejpam-1375	343	21	modeling	modeling	NOUN
ejpam-1375	343	22	.	.	PUNCT
ejpam-1375	344	1	that	that	PRON
ejpam-1375	344	2	is	is	ADV
ejpam-1375	344	3	,	,	PUNCT
ejpam-1375	344	4	we	we	PRON
ejpam-1375	344	5	showed	show	VERB
ejpam-1375	344	6	how	how	SCONJ
ejpam-1375	344	7	to	to	PART
ejpam-1375	344	8	select	select	VERB
ejpam-1375	344	9	a	a	DET
ejpam-1375	344	10	best	good	ADJ
ejpam-1375	344	11	subset	subset	NOUN
ejpam-1375	344	12	of	of	ADP
ejpam-1375	344	13	variables	variable	NOUN
ejpam-1375	344	14	in	in	ADP
ejpam-1375	344	15	a	a	DET
ejpam-1375	344	16	regression	regression	NOUN
ejpam-1375	344	17	model	model	NOUN
ejpam-1375	344	18	using	use	VERB
ejpam-1375	344	19	the	the	DET
ejpam-1375	344	20	genetic	genetic	ADJ
ejpam-1375	344	21	algorithm	algorithm	NOUN
ejpam-1375	344	22	(	(	PUNCT
ejpam-1375	344	23	ga	ga	PROPN
ejpam-1375	344	24	)	)	PUNCT
ejpam-1375	344	25	.	.	PUNCT
ejpam-1375	345	1	in	in	ADP
ejpam-1375	345	2	this	this	DET
ejpam-1375	345	3	context	context	NOUN
ejpam-1375	345	4	,	,	PUNCT
ejpam-1375	345	5	we	we	PRON
ejpam-1375	345	6	scored	score	VERB
ejpam-1375	345	7	several	several	ADJ
ejpam-1375	345	8	aic	aic	PROPN
ejpam-1375	345	9	and	and	CCONJ
ejpam-1375	345	10	icom	icom	PROPN
ejpam-1375	345	11	p	p	ADJ
ejpam-1375	345	12	-	-	PUNCT
ejpam-1375	345	13	type	type	NOUN
ejpam-1375	345	14	criteria	criterion	NOUN
ejpam-1375	345	15	to	to	PART
ejpam-1375	345	16	evaluate	evaluate	VERB
ejpam-1375	345	17	the	the	DET
ejpam-1375	345	18	hybrid	hybrid	ADJ
ejpam-1375	345	19	rbf	rbf	PROPN
ejpam-1375	345	20	model	model	NOUN
ejpam-1375	345	21	,	,	PUNCT
ejpam-1375	345	22	rbf	rbf	PROPN
ejpam-1375	345	23	-	-	PUNCT
ejpam-1375	345	24	nn	nn	PROPN
ejpam-1375	345	25	combined	combine	VERB
ejpam-1375	345	26	with	with	ADP
ejpam-1375	345	27	regression	regression	NOUN
ejpam-1375	345	28	trees	tree	NOUN
ejpam-1375	345	29	using	use	VERB
ejpam-1375	345	30	ridge	ridge	NOUN
ejpam-1375	345	31	regression	regression	NOUN
ejpam-1375	345	32	regularization	regularization	NOUN
ejpam-1375	345	33	.	.	PUNCT
ejpam-1375	346	1	we	we	PRON
ejpam-1375	346	2	used	use	VERB
ejpam-1375	346	3	a	a	DET
ejpam-1375	346	4	highly	highly	ADV
ejpam-1375	346	5	nonlinear	nonlinear	ADJ
ejpam-1375	346	6	simulation	simulation	NOUN
ejpam-1375	346	7	protocol	protocol	NOUN
ejpam-1375	346	8	that	that	PRON
ejpam-1375	346	9	shows	show	VERB
ejpam-1375	346	10	the	the	DET
ejpam-1375	346	11	nonlinear	nonlinear	ADJ
ejpam-1375	346	12	functional	functional	ADJ
ejpam-1375	346	13	relationship	relationship	NOUN
ejpam-1375	346	14	between	between	ADP
ejpam-1375	346	15	input	input	NOUN
ejpam-1375	346	16	and	and	CCONJ
ejpam-1375	346	17	output	output	NOUN
ejpam-1375	346	18	variables	variable	NOUN
ejpam-1375	346	19	and	and	CCONJ
ejpam-1375	346	20	that	that	PRON
ejpam-1375	346	21	of	of	ADP
ejpam-1375	346	22	some	some	DET
ejpam-1375	346	23	redundant	redundant	ADJ
ejpam-1375	346	24	variables	variable	NOUN
ejpam-1375	346	25	.	.	PUNCT
ejpam-1375	347	1	simulation	simulation	NOUN
ejpam-1375	347	2	results	result	NOUN
ejpam-1375	347	3	show	show	VERB
ejpam-1375	347	4	that	that	SCONJ
ejpam-1375	347	5	gaussian	gaussian	ADJ
ejpam-1375	347	6	kernel	kernel	NOUN
ejpam-1375	347	7	function	function	NOUN
ejpam-1375	347	8	is	be	AUX
ejpam-1375	347	9	the	the	DET
ejpam-1375	347	10	best	good	ADJ
ejpam-1375	347	11	choice	choice	NOUN
ejpam-1375	347	12	for	for	ADP
ejpam-1375	347	13	hidden	hide	VERB
ejpam-1375	347	14	unit	unit	NOUN
ejpam-1375	347	15	transfer	transfer	NOUN
ejpam-1375	347	16	function	function	NOUN
ejpam-1375	347	17	of	of	ADP
ejpam-1375	347	18	hybrid	hybrid	ADJ
ejpam-1375	347	19	rbf	rbf	PROPN
ejpam-1375	347	20	-	-	PUNCT
ejpam-1375	347	21	nn	nn	PROPN
ejpam-1375	347	22	.	.	PROPN
ejpam-1375	347	23	on	on	ADP
ejpam-1375	347	24	the	the	DET
ejpam-1375	347	25	other	other	ADJ
ejpam-1375	347	26	hand	hand	NOUN
ejpam-1375	347	27	,	,	PUNCT
ejpam-1375	347	28	model	model	NOUN
ejpam-1375	347	29	selection	selection	NOUN
ejpam-1375	347	30	performance	performance	NOUN
ejpam-1375	347	31	of	of	ADP
ejpam-1375	347	32	hybrid	hybrid	ADJ
ejpam-1375	347	33	rbf	rbf	PROPN
ejpam-1375	347	34	-	-	PUNCT
ejpam-1375	347	35	nn	nn	PROPN
ejpam-1375	347	36	model	model	NOUN
ejpam-1375	347	37	is	be	AUX
ejpam-1375	347	38	much	much	ADV
ejpam-1375	347	39	more	more	ADV
ejpam-1375	347	40	superior	superior	ADJ
ejpam-1375	347	41	than	than	ADP
ejpam-1375	347	42	the	the	DET
ejpam-1375	347	43	linear	linear	PROPN
ejpam-1375	347	44	regression	regression	NOUN
ejpam-1375	347	45	model	model	NOUN
ejpam-1375	347	46	.	.	PUNCT
ejpam-1375	348	1	in	in	ADP
ejpam-1375	348	2	fact	fact	NOUN
ejpam-1375	348	3	the	the	DET
ejpam-1375	348	4	usual	usual	ADJ
ejpam-1375	348	5	standard	standard	ADJ
ejpam-1375	348	6	linear	linear	ADJ
ejpam-1375	348	7	regression	regression	NOUN
ejpam-1375	348	8	model	model	NOUN
ejpam-1375	348	9	fails	fail	VERB
ejpam-1375	348	10	miserably	miserably	ADV
ejpam-1375	348	11	when	when	SCONJ
ejpam-1375	348	12	the	the	DET
ejpam-1375	348	13	data	datum	NOUN
ejpam-1375	348	14	exhibits	exhibit	VERB
ejpam-1375	348	15	highly	highly	ADV
ejpam-1375	348	16	nonlinear	nonlinear	ADJ
ejpam-1375	348	17	structure	structure	NOUN
ejpam-1375	348	18	.	.	PUNCT
ejpam-1375	349	1	the	the	DET
ejpam-1375	349	2	success	success	NOUN
ejpam-1375	349	3	of	of	ADP
ejpam-1375	349	4	hybrid	hybrid	ADJ
ejpam-1375	349	5	rbf	rbf	PROPN
ejpam-1375	349	6	-	-	PUNCT
ejpam-1375	349	7	nn	nn	PROPN
ejpam-1375	349	8	model	model	NOUN
ejpam-1375	349	9	using	use	VERB
ejpam-1375	349	10	model	model	NOUN
ejpam-1375	349	11	selection	selection	NOUN
ejpam-1375	349	12	criteria	criterion	NOUN
ejpam-1375	349	13	as	as	ADP
ejpam-1375	349	14	a	a	DET
ejpam-1375	349	15	fitness	fitness	NOUN
ejpam-1375	349	16	function	function	NOUN
ejpam-1375	349	17	consistently	consistently	ADV
ejpam-1375	349	18	improves	improve	VERB
ejpam-1375	349	19	as	as	ADP
ejpam-1375	349	20	the	the	DET
ejpam-1375	349	21	sample	sample	NOUN
ejpam-1375	349	22	size	size	NOUN
ejpam-1375	349	23	increases	increase	NOUN
ejpam-1375	349	24	.	.	PUNCT
ejpam-1375	350	1	finally	finally	ADV
ejpam-1375	350	2	,	,	PUNCT
ejpam-1375	350	3	estimation	estimation	NOUN
ejpam-1375	350	4	and	and	CCONJ
ejpam-1375	350	5	prediction	prediction	NOUN
ejpam-1375	350	6	performance	performance	NOUN
ejpam-1375	350	7	of	of	ADP
ejpam-1375	350	8	hybrid	hybrid	ADJ
ejpam-1375	350	9	rbf	rbf	PROPN
ejpam-1375	350	10	-	-	PUNCT
ejpam-1375	350	11	nn	nn	PROPN
ejpam-1375	350	12	models	model	NOUN
ejpam-1375	350	13	is	be	AUX
ejpam-1375	350	14	measured	measure	VERB
ejpam-1375	350	15	with	with	ADP
ejpam-1375	350	16	respect	respect	NOUN
ejpam-1375	350	17	to	to	ADP
ejpam-1375	350	18	rmse	rmse	NOUN
ejpam-1375	350	19	and	and	CCONJ
ejpam-1375	350	20	rmspe	rmspe	NOUN
ejpam-1375	350	21	using	use	VERB
ejpam-1375	350	22	the	the	DET
ejpam-1375	350	23	best	good	ADJ
ejpam-1375	350	24	subset	subset	NOUN
ejpam-1375	350	25	of	of	ADP
ejpam-1375	350	26	predictor	predictor	NOUN
ejpam-1375	350	27	variables	variable	NOUN
ejpam-1375	350	28	chosen	choose	VERB
ejpam-1375	350	29	.	.	PUNCT
ejpam-1375	351	1	our	our	PRON
ejpam-1375	351	2	results	result	NOUN
ejpam-1375	351	3	show	show	VERB
ejpam-1375	351	4	that	that	SCONJ
ejpam-1375	351	5	,	,	PUNCT
ejpam-1375	351	6	hybrid	hybrid	ADJ
ejpam-1375	351	7	rbf	rbf	PROPN
ejpam-1375	351	8	-	-	PUNCT
ejpam-1375	351	9	nn	nn	PROPN
ejpam-1375	351	10	model	model	NOUN
ejpam-1375	351	11	is	be	AUX
ejpam-1375	351	12	quite	quite	ADV
ejpam-1375	351	13	adoptive	adoptive	ADJ
ejpam-1375	351	14	to	to	PART
ejpam-1375	351	15	handle	handle	VERB
ejpam-1375	351	16	highly	highly	ADV
ejpam-1375	351	17	nonlinear	nonlinear	ADJ
ejpam-1375	351	18	relationships	relationship	NOUN
ejpam-1375	351	19	between	between	ADP
ejpam-1375	351	20	the	the	DET
ejpam-1375	351	21	predictor	predictor	NOUN
ejpam-1375	351	22	and	and	CCONJ
ejpam-1375	351	23	response	response	NOUN
ejpam-1375	351	24	variables	variable	NOUN
ejpam-1375	351	25	in	in	ADP
ejpam-1375	351	26	regression	regression	NOUN
ejpam-1375	351	27	modeling	modeling	NOUN
ejpam-1375	351	28	.	.	PUNCT
ejpam-1375	352	1	it	it	PRON
ejpam-1375	352	2	would	would	AUX
ejpam-1375	352	3	be	be	AUX
ejpam-1375	352	4	interesting	interesting	ADJ
ejpam-1375	352	5	to	to	PART
ejpam-1375	352	6	extend	extend	VERB
ejpam-1375	352	7	this	this	DET
ejpam-1375	352	8	work	work	NOUN
ejpam-1375	352	9	to	to	ADP
ejpam-1375	352	10	the	the	DET
ejpam-1375	352	11	multivariate	multivariate	NOUN
ejpam-1375	352	12	case	case	NOUN
ejpam-1375	352	13	where	where	SCONJ
ejpam-1375	352	14	we	we	PRON
ejpam-1375	352	15	have	have	VERB
ejpam-1375	352	16	more	more	ADJ
ejpam-1375	352	17	than	than	ADP
ejpam-1375	352	18	one	one	NUM
ejpam-1375	352	19	response	response	NOUN
ejpam-1375	352	20	variable	variable	NOUN
ejpam-1375	352	21	.	.	PUNCT
ejpam-1375	353	1	this	this	DET
ejpam-1375	353	2	work	work	NOUN
ejpam-1375	353	3	within	within	ADP
ejpam-1375	353	4	rbf	rbf	PROPN
ejpam-1375	353	5	-	-	PUNCT
ejpam-1375	353	6	nn	nn	PROPN
ejpam-1375	353	7	modeling	modeling	NOUN
ejpam-1375	353	8	framework	framework	NOUN
ejpam-1375	353	9	has	have	AUX
ejpam-1375	353	10	not	not	PART
ejpam-1375	353	11	been	be	AUX
ejpam-1375	353	12	carried	carry	VERB
ejpam-1375	353	13	out	out	ADP
ejpam-1375	353	14	before	before	ADV
ejpam-1375	353	15	.	.	PUNCT
ejpam-1375	354	1	we	we	PRON
ejpam-1375	354	2	intend	intend	VERB
ejpam-1375	354	3	to	to	PART
ejpam-1375	354	4	pursue	pursue	VERB
ejpam-1375	354	5	this	this	DET
ejpam-1375	354	6	avenue	avenue	NOUN
ejpam-1375	354	7	in	in	ADP
ejpam-1375	354	8	a	a	DET
ejpam-1375	354	9	future	future	ADJ
ejpam-1375	354	10	research	research	NOUN
ejpam-1375	354	11	initiative	initiative	NOUN
ejpam-1375	354	12	.	.	PUNCT
ejpam-1375	355	1	acknowledgements	acknowledgement	NOUN
ejpam-1375	355	2	the	the	DET
ejpam-1375	355	3	first	first	ADJ
ejpam-1375	355	4	author	author	NOUN
ejpam-1375	355	5	extends	extend	VERB
ejpam-1375	355	6	his	his	PRON
ejpam-1375	355	7	thanks	thank	NOUN
ejpam-1375	355	8	to	to	ADP
ejpam-1375	355	9	the	the	DET
ejpam-1375	355	10	scientific	scientific	ADJ
ejpam-1375	355	11	and	and	CCONJ
ejpam-1375	355	12	technological	technological	ADJ
ejpam-1375	355	13	research	research	NOUN
ejpam-1375	355	14	council	council	NOUN
ejpam-1375	355	15	of	of	ADP
ejpam-1375	355	16	turkey	turkey	PROPN
ejpam-1375	355	17	(	(	PUNCT
ejpam-1375	355	18	tubitak	tubitak	NOUN
ejpam-1375	355	19	)	)	PUNCT
ejpam-1375	355	20	for	for	ADP
ejpam-1375	355	21	their	their	PRON
ejpam-1375	355	22	support	support	NOUN
ejpam-1375	355	23	for	for	ADP
ejpam-1375	355	24	young	young	ADJ
ejpam-1375	355	25	researchers	researcher	NOUN
ejpam-1375	355	26	award	award	NOUN
ejpam-1375	355	27	.	.	PUNCT
ejpam-1375	356	1	the	the	DET
ejpam-1375	356	2	first	first	ADJ
ejpam-1375	356	3	author	author	NOUN
ejpam-1375	356	4	is	be	AUX
ejpam-1375	356	5	grateful	grateful	ADJ
ejpam-1375	356	6	to	to	PART
ejpam-1375	356	7	prof	prof	VERB
ejpam-1375	356	8	.	.	PUNCT
ejpam-1375	357	1	dr	dr	PROPN
ejpam-1375	357	2	.	.	PROPN
ejpam-1375	357	3	bozdogan	bozdogan	PROPN
ejpam-1375	357	4	for	for	ADP
ejpam-1375	357	5	giving	give	VERB
ejpam-1375	357	6	this	this	DET
ejpam-1375	357	7	problem	problem	NOUN
ejpam-1375	357	8	and	and	CCONJ
ejpam-1375	357	9	sharing	share	VERB
ejpam-1375	357	10	his	his	PRON
ejpam-1375	357	11	initial	initial	ADJ
ejpam-1375	357	12	results	result	NOUN
ejpam-1375	357	13	on	on	ADP
ejpam-1375	357	14	rbf	rbf	PROPN
ejpam-1375	357	15	-	-	PUNCT
ejpam-1375	357	16	nn	nn	PROPN
ejpam-1375	357	17	.	.	PROPN
ejpam-1375	357	18	without	without	ADP
ejpam-1375	357	19	his	his	PRON
ejpam-1375	357	20	supervision	supervision	NOUN
ejpam-1375	357	21	and	and	CCONJ
ejpam-1375	357	22	guidance	guidance	NOUN
ejpam-1375	357	23	this	this	DET
ejpam-1375	357	24	research	research	NOUN
ejpam-1375	357	25	would	would	AUX
ejpam-1375	357	26	not	not	PART
ejpam-1375	357	27	have	have	AUX
ejpam-1375	357	28	been	be	AUX
ejpam-1375	357	29	possible	possible	ADJ
ejpam-1375	357	30	.	.	PUNCT
ejpam-1375	358	1	therefore	therefore	ADV
ejpam-1375	358	2	,	,	PUNCT
ejpam-1375	358	3	the	the	DET
ejpam-1375	358	4	first	first	ADJ
ejpam-1375	358	5	author	author	NOUN
ejpam-1375	358	6	is	be	AUX
ejpam-1375	358	7	gratefully	gratefully	ADV
ejpam-1375	358	8	acknowledges	acknowledge	VERB
ejpam-1375	358	9	prof	prof	PROPN
ejpam-1375	358	10	.	.	PUNCT
ejpam-1375	359	1	dr	dr	PROPN
ejpam-1375	359	2	.	.	PROPN
ejpam-1375	359	3	bozdogan	bozdogan	PROPN
ejpam-1375	359	4	’s	’s	PART
ejpam-1375	359	5	guidance	guidance	NOUN
ejpam-1375	359	6	.	.	PUNCT
ejpam-1375	360	1	references	reference	NOUN
ejpam-1375	360	2	[	[	X
ejpam-1375	360	3	1	1	NUM
ejpam-1375	360	4	]	]	PUNCT
ejpam-1375	360	5	h	h	NOUN
ejpam-1375	360	6	akaike	akaike	ADJ
ejpam-1375	360	7	.	.	PUNCT
ejpam-1375	361	1	information	information	NOUN
ejpam-1375	361	2	theory	theory	NOUN
ejpam-1375	361	3	and	and	CCONJ
ejpam-1375	361	4	an	an	DET
ejpam-1375	361	5	extension	extension	NOUN
ejpam-1375	361	6	of	of	ADP
ejpam-1375	361	7	the	the	DET
ejpam-1375	361	8	maximum	maximum	ADJ
ejpam-1375	361	9	likelihood	likelihood	NOUN
ejpam-1375	361	10	principle	principle	NOUN
ejpam-1375	361	11	.	.	PUNCT
ejpam-1375	362	1	in	in	ADP
ejpam-1375	362	2	b	b	PROPN
ejpam-1375	362	3	petrox	petrox	NOUN
ejpam-1375	362	4	and	and	CCONJ
ejpam-1375	362	5	f	f	PROPN
ejpam-1375	362	6	csaki	csaki	NOUN
ejpam-1375	362	7	,	,	PUNCT
ejpam-1375	362	8	editors	editor	NOUN
ejpam-1375	362	9	,	,	PUNCT
ejpam-1375	362	10	second	second	ADJ
ejpam-1375	362	11	international	international	ADJ
ejpam-1375	362	12	symposium	symposium	NOUN
ejpam-1375	362	13	on	on	ADP
ejpam-1375	362	14	information	information	NOUN
ejpam-1375	362	15	theory	theory	NOUN
ejpam-1375	362	16	.	.	PUNCT
ejpam-1375	362	17	,	,	PUNCT
ejpam-1375	362	18	pages	page	NOUN
ejpam-1375	362	19	267–281	267–281	NUM
ejpam-1375	362	20	,	,	PUNCT
ejpam-1375	362	21	budapest	budapest	NOUN
ejpam-1375	362	22	,	,	PUNCT
ejpam-1375	362	23	1973	1973	NUM
ejpam-1375	362	24	.	.	PUNCT
ejpam-1375	363	1	academiai	academiai	PROPN
ejpam-1375	363	2	kiado	kiado	PROPN
ejpam-1375	363	3	.	.	PUNCT
ejpam-1375	364	1	[	[	X
ejpam-1375	364	2	2	2	NUM
ejpam-1375	364	3	]	]	X
ejpam-1375	364	4	c	c	PROPN
ejpam-1375	364	5	bishop	bishop	PROPN
ejpam-1375	364	6	.	.	PUNCT
ejpam-1375	365	1	improving	improve	VERB
ejpam-1375	365	2	the	the	DET
ejpam-1375	365	3	generalization	generalization	NOUN
ejpam-1375	365	4	properties	property	NOUN
ejpam-1375	365	5	of	of	ADP
ejpam-1375	365	6	radial	radial	ADJ
ejpam-1375	365	7	basis	basis	NOUN
ejpam-1375	365	8	function	function	NOUN
ejpam-1375	365	9	neural	neural	ADJ
ejpam-1375	365	10	networks	network	NOUN
ejpam-1375	365	11	.	.	PUNCT
ejpam-1375	366	1	neural	neural	ADJ
ejpam-1375	366	2	computation	computation	NOUN
ejpam-1375	366	3	,	,	PUNCT
ejpam-1375	366	4	3:579–588	3:579–588	NUM
ejpam-1375	366	5	,	,	PUNCT
ejpam-1375	366	6	1991	1991	NUM
ejpam-1375	366	7	.	.	PUNCT
ejpam-1375	367	1	[	[	X
ejpam-1375	367	2	3	3	NUM
ejpam-1375	367	3	]	]	X
ejpam-1375	367	4	d	d	PROPN
ejpam-1375	367	5	boyce	boyce	PROPN
ejpam-1375	367	6	,	,	PUNCT
ejpam-1375	367	7	a	a	DET
ejpam-1375	367	8	fahri	fahri	NOUN
ejpam-1375	367	9	,	,	PUNCT
ejpam-1375	367	10	and	and	CCONJ
ejpam-1375	367	11	r	r	NOUN
ejpam-1375	367	12	weischedel	weischedel	NOUN
ejpam-1375	367	13	.	.	PUNCT
ejpam-1375	368	1	optimal	optimal	ADJ
ejpam-1375	368	2	subset	subset	NOUN
ejpam-1375	368	3	selection	selection	NOUN
ejpam-1375	368	4	:	:	PUNCT
ejpam-1375	368	5	multiple	multiple	ADJ
ejpam-1375	368	6	regression	regression	NOUN
ejpam-1375	368	7	,	,	PUNCT
ejpam-1375	368	8	independence	independence	NOUN
ejpam-1375	368	9	,	,	PUNCT
ejpam-1375	368	10	and	and	CCONJ
ejpam-1375	368	11	optimal	optimal	ADJ
ejpam-1375	368	12	network	network	NOUN
ejpam-1375	368	13	algorithms	algorithms	NOUN
ejpam-1375	368	14	extension	extension	NOUN
ejpam-1375	368	15	.	.	PUNCT
ejpam-1375	369	1	springer	springer	NOUN
ejpam-1375	369	2	verlag	verlag	PROPN
ejpam-1375	369	3	,	,	PUNCT
ejpam-1375	369	4	new	new	PROPN
ejpam-1375	369	5	york	york	PROPN
ejpam-1375	369	6	,	,	PUNCT
ejpam-1375	369	7	1974	1974	NUM
ejpam-1375	369	8	.	.	PUNCT
ejpam-1375	370	1	[	[	X
ejpam-1375	370	2	4	4	NUM
ejpam-1375	370	3	]	]	X
ejpam-1375	370	4	h	h	NOUN
ejpam-1375	370	5	bozdogan	bozdogan	NOUN
ejpam-1375	370	6	.	.	PUNCT
ejpam-1375	371	1	model	model	NOUN
ejpam-1375	371	2	selection	selection	NOUN
ejpam-1375	371	3	and	and	CCONJ
ejpam-1375	371	4	akaike	akaike	NOUN
ejpam-1375	371	5	’s	’s	PART
ejpam-1375	371	6	information	information	NOUN
ejpam-1375	371	7	criterion	criterion	NOUN
ejpam-1375	371	8	(	(	PUNCT
ejpam-1375	371	9	aic	aic	PROPN
ejpam-1375	371	10	):	):	PUNCT
ejpam-1375	371	11	the	the	DET
ejpam-1375	371	12	general	general	ADJ
ejpam-1375	371	13	theory	theory	NOUN
ejpam-1375	371	14	and	and	CCONJ
ejpam-1375	371	15	it	it	PRON
ejpam-1375	371	16	’s	’	VERB
ejpam-1375	371	17	analytical	analytical	ADJ
ejpam-1375	371	18	extension	extension	NOUN
ejpam-1375	371	19	.	.	PUNCT
ejpam-1375	372	1	journal	journal	PROPN
ejpam-1375	372	2	of	of	ADP
ejpam-1375	372	3	mathematical	mathematical	ADJ
ejpam-1375	372	4	psychology	psychology	NOUN
ejpam-1375	372	5	,	,	PUNCT
ejpam-1375	372	6	52:345–370	52:345–370	NUM
ejpam-1375	372	7	,	,	PUNCT
ejpam-1375	372	8	september	september	PROPN
ejpam-1375	372	9	1987	1987	NUM
ejpam-1375	372	10	.	.	PUNCT
ejpam-1375	373	1	references	reference	NOUN
ejpam-1375	373	2	485	485	NUM
ejpam-1375	373	3	[	[	X
ejpam-1375	373	4	5	5	NUM
ejpam-1375	373	5	]	]	PUNCT
ejpam-1375	373	6	h	h	NOUN
ejpam-1375	373	7	bozdogan	bozdogan	NOUN
ejpam-1375	373	8	.	.	PUNCT
ejpam-1375	374	1	icomp	icomp	PROPN
ejpam-1375	374	2	:	:	PUNCT
ejpam-1375	374	3	a	a	DET
ejpam-1375	374	4	new	new	ADJ
ejpam-1375	374	5	model	model	ADJ
ejpam-1375	374	6	-	-	PUNCT
ejpam-1375	374	7	selection	selection	NOUN
ejpam-1375	374	8	criteria	criterion	NOUN
ejpam-1375	374	9	.	.	PUNCT
ejpam-1375	375	1	in	in	ADP
ejpam-1375	375	2	h.h	h.h	PROPN
ejpam-1375	375	3	bock	bock	NOUN
ejpam-1375	375	4	,	,	PUNCT
ejpam-1375	375	5	editor	editor	NOUN
ejpam-1375	375	6	,	,	PUNCT
ejpam-1375	375	7	classification	classification	NOUN
ejpam-1375	375	8	and	and	CCONJ
ejpam-1375	375	9	related	related	ADJ
ejpam-1375	375	10	methods	method	NOUN
ejpam-1375	375	11	of	of	ADP
ejpam-1375	375	12	data	datum	NOUN
ejpam-1375	375	13	analysis	analysis	NOUN
ejpam-1375	375	14	.	.	PUNCT
ejpam-1375	375	15	1988	1988	NUM
ejpam-1375	375	16	.	.	PUNCT
ejpam-1375	376	1	[	[	X
ejpam-1375	376	2	6	6	NUM
ejpam-1375	376	3	]	]	PUNCT
ejpam-1375	376	4	h	h	NOUN
ejpam-1375	376	5	bozdogan	bozdogan	NOUN
ejpam-1375	376	6	.	.	PUNCT
ejpam-1375	377	1	mixture	mixture	NOUN
ejpam-1375	377	2	-	-	PUNCT
ejpam-1375	377	3	model	model	NOUN
ejpam-1375	377	4	cluster	cluster	NOUN
ejpam-1375	377	5	analysis	analysis	NOUN
ejpam-1375	377	6	using	use	VERB
ejpam-1375	377	7	a	a	DET
ejpam-1375	377	8	new	new	ADJ
ejpam-1375	377	9	informational	informational	ADJ
ejpam-1375	377	10	complexity	complexity	NOUN
ejpam-1375	377	11	and	and	CCONJ
ejpam-1375	377	12	model	model	NOUN
ejpam-1375	377	13	selection	selection	NOUN
ejpam-1375	377	14	criteria	criterion	NOUN
ejpam-1375	377	15	.	.	PUNCT
ejpam-1375	378	1	in	in	ADP
ejpam-1375	378	2	h	h	NOUN
ejpam-1375	378	3	bozdogan	bozdogan	NOUN
ejpam-1375	378	4	,	,	PUNCT
ejpam-1375	378	5	editor	editor	NOUN
ejpam-1375	378	6	,	,	PUNCT
ejpam-1375	378	7	multivariate	multivariate	VERB
ejpam-1375	378	8	statistical	statistical	ADJ
ejpam-1375	378	9	modeling	modeling	NOUN
ejpam-1375	378	10	,	,	PUNCT
ejpam-1375	378	11	vol	vol	NOUN
ejpam-1375	378	12	.	.	PROPN
ejpam-1375	379	1	2	2	NUM
ejpam-1375	379	2	,	,	PUNCT
ejpam-1375	379	3	proceedings	proceeding	NOUN
ejpam-1375	379	4	of	of	ADP
ejpam-1375	379	5	the	the	DET
ejpam-1375	379	6	first	first	ADJ
ejpam-1375	379	7	us	us	PROPN
ejpam-1375	379	8	/	/	SYM
ejpam-1375	379	9	japan	japan	PROPN
ejpam-1375	379	10	conference	conference	PROPN
ejpam-1375	379	11	on	on	ADP
ejpam-1375	379	12	the	the	DET
ejpam-1375	379	13	frontiers	frontier	NOUN
ejpam-1375	379	14	of	of	ADP
ejpam-1375	379	15	statistical	statistical	ADJ
ejpam-1375	379	16	modeling	modeling	NOUN
ejpam-1375	379	17	:	:	PUNCT
ejpam-1375	379	18	an	an	DET
ejpam-1375	379	19	informational	informational	ADJ
ejpam-1375	379	20	approach	approach	NOUN
ejpam-1375	379	21	,	,	PUNCT
ejpam-1375	379	22	pages	page	NOUN
ejpam-1375	379	23	69–113	69–113	PROPN
ejpam-1375	379	24	.	.	PUNCT
ejpam-1375	380	1	kluwer	kluwer	PROPN
ejpam-1375	380	2	academic	academic	ADJ
ejpam-1375	380	3	publishers	publisher	NOUN
ejpam-1375	380	4	,	,	PUNCT
ejpam-1375	380	5	the	the	DET
ejpam-1375	380	6	netherlands	netherlands	PROPN
ejpam-1375	380	7	,	,	PUNCT
ejpam-1375	380	8	dordrecht	dordrecht	PROPN
ejpam-1375	380	9	,	,	PUNCT
ejpam-1375	380	10	1994	1994	NUM
ejpam-1375	380	11	.	.	PUNCT
ejpam-1375	381	1	[	[	X
ejpam-1375	381	2	7	7	NUM
ejpam-1375	381	3	]	]	X
ejpam-1375	381	4	h	h	NOUN
ejpam-1375	381	5	bozdogan	bozdogan	NOUN
ejpam-1375	381	6	.	.	PUNCT
ejpam-1375	382	1	akaike	akaike	ADP
ejpam-1375	382	2	’s	’s	PART
ejpam-1375	382	3	information	information	NOUN
ejpam-1375	382	4	criterion	criterion	NOUN
ejpam-1375	382	5	and	and	CCONJ
ejpam-1375	382	6	recent	recent	ADJ
ejpam-1375	382	7	developments	development	NOUN
ejpam-1375	382	8	in	in	ADP
ejpam-1375	382	9	informational	informational	ADJ
ejpam-1375	382	10	complexity	complexity	NOUN
ejpam-1375	382	11	.	.	PUNCT
ejpam-1375	383	1	journal	journal	PROPN
ejpam-1375	383	2	of	of	ADP
ejpam-1375	383	3	mathematical	mathematical	ADJ
ejpam-1375	383	4	psychology	psychology	NOUN
ejpam-1375	383	5	,	,	PUNCT
ejpam-1375	383	6	44:62–91	44:62–91	NUM
ejpam-1375	383	7	,	,	PUNCT
ejpam-1375	383	8	march	march	PROPN
ejpam-1375	383	9	2000	2000	NUM
ejpam-1375	383	10	.	.	PUNCT
ejpam-1375	384	1	[	[	X
ejpam-1375	384	2	8	8	NUM
ejpam-1375	384	3	]	]	X
ejpam-1375	384	4	h	h	NOUN
ejpam-1375	384	5	bozdogan	bozdogan	NOUN
ejpam-1375	384	6	.	.	PUNCT
ejpam-1375	385	1	intelligent	intelligent	ADJ
ejpam-1375	385	2	statistical	statistical	ADJ
ejpam-1375	385	3	data	datum	NOUN
ejpam-1375	385	4	mining	mining	NOUN
ejpam-1375	385	5	with	with	ADP
ejpam-1375	385	6	information	information	NOUN
ejpam-1375	385	7	complexity	complexity	NOUN
ejpam-1375	385	8	and	and	CCONJ
ejpam-1375	385	9	genetic	genetic	ADJ
ejpam-1375	385	10	algorithms	algorithm	NOUN
ejpam-1375	385	11	.	.	PUNCT
ejpam-1375	386	1	in	in	ADP
ejpam-1375	386	2	h	h	NOUN
ejpam-1375	386	3	bozdogan	bozdogan	NOUN
ejpam-1375	386	4	,	,	PUNCT
ejpam-1375	386	5	editor	editor	NOUN
ejpam-1375	386	6	,	,	PUNCT
ejpam-1375	386	7	statistical	statistical	ADJ
ejpam-1375	386	8	data	data	NOUN
ejpam-1375	386	9	mining	mining	NOUN
ejpam-1375	386	10	and	and	CCONJ
ejpam-1375	386	11	knowledge	knowledge	NOUN
ejpam-1375	386	12	discovery	discovery	NOUN
ejpam-1375	386	13	,	,	PUNCT
ejpam-1375	386	14	pages	page	NOUN
ejpam-1375	386	15	15–56	15–56	NUM
ejpam-1375	386	16	.	.	PUNCT
ejpam-1375	387	1	chapman	chapman	PROPN
ejpam-1375	387	2	and	and	CCONJ
ejpam-1375	387	3	hall	hall	PROPN
ejpam-1375	387	4	/	/	SYM
ejpam-1375	387	5	crc	crc	PROPN
ejpam-1375	387	6	,	,	PUNCT
ejpam-1375	387	7	boca	boca	PROPN
ejpam-1375	387	8	raton	raton	PROPN
ejpam-1375	387	9	,	,	PUNCT
ejpam-1375	387	10	florida	florida	PROPN
ejpam-1375	387	11	,	,	PUNCT
ejpam-1375	387	12	2004	2004	NUM
ejpam-1375	387	13	.	.	PUNCT
ejpam-1375	388	1	[	[	X
ejpam-1375	388	2	9	9	NUM
ejpam-1375	388	3	]	]	SYM
ejpam-1375	388	4	l	l	NOUN
ejpam-1375	388	5	breiman	breiman	NOUN
ejpam-1375	388	6	,	,	PUNCT
ejpam-1375	388	7	j	j	PROPN
ejpam-1375	388	8	freidman	freidman	PROPN
ejpam-1375	388	9	,	,	PUNCT
ejpam-1375	388	10	j	j	PROPN
ejpam-1375	388	11	c	c	PROPN
ejpam-1375	388	12	stone	stone	NOUN
ejpam-1375	388	13	,	,	PUNCT
ejpam-1375	388	14	and	and	CCONJ
ejpam-1375	388	15	r	r	PROPN
ejpam-1375	388	16	olsen	olsen	NOUN
ejpam-1375	388	17	.	.	PUNCT
ejpam-1375	389	1	classification	classification	NOUN
ejpam-1375	389	2	and	and	CCONJ
ejpam-1375	389	3	regression	regression	NOUN
ejpam-1375	389	4	trees	tree	NOUN
ejpam-1375	389	5	.	.	PUNCT
ejpam-1375	390	1	chapman	chapman	PROPN
ejpam-1375	390	2	and	and	CCONJ
ejpam-1375	390	3	hall	hall	PROPN
ejpam-1375	390	4	,	,	PUNCT
ejpam-1375	390	5	1984	1984	NUM
ejpam-1375	390	6	.	.	PUNCT
ejpam-1375	391	1	[	[	X
ejpam-1375	391	2	10	10	NUM
ejpam-1375	391	3	]	]	X
ejpam-1375	391	4	r	r	NOUN
ejpam-1375	391	5	hocking	hocking	NOUN
ejpam-1375	391	6	.	.	PUNCT
ejpam-1375	392	1	developments	development	NOUN
ejpam-1375	392	2	in	in	ADP
ejpam-1375	392	3	linear	linear	PROPN
ejpam-1375	392	4	regression	regression	NOUN
ejpam-1375	392	5	methodology	methodology	NOUN
ejpam-1375	392	6	:	:	PUNCT
ejpam-1375	392	7	1959	1959	NUM
ejpam-1375	392	8	-	-	SYM
ejpam-1375	392	9	1982	1982	NUM
ejpam-1375	392	10	.	.	PUNCT
ejpam-1375	393	1	technometrics	technometric	NOUN
ejpam-1375	393	2	,	,	PUNCT
ejpam-1375	393	3	25:219–230	25:219–230	NUM
ejpam-1375	393	4	,	,	PUNCT
ejpam-1375	393	5	1983	1983	NUM
ejpam-1375	393	6	.	.	PUNCT
ejpam-1375	394	1	[	[	X
ejpam-1375	394	2	11	11	NUM
ejpam-1375	394	3	]	]	PUNCT
ejpam-1375	394	4	a	a	DET
ejpam-1375	394	5	horel	horel	NOUN
ejpam-1375	394	6	,	,	PUNCT
ejpam-1375	394	7	r	r	NOUN
ejpam-1375	394	8	kennard	kennard	NOUN
ejpam-1375	394	9	,	,	PUNCT
ejpam-1375	394	10	and	and	CCONJ
ejpam-1375	394	11	k	k	PROPN
ejpam-1375	394	12	baldwin	baldwin	PROPN
ejpam-1375	394	13	.	.	PROPN
ejpam-1375	395	1	ridge	ridge	PROPN
ejpam-1375	395	2	regression	regression	PROPN
ejpam-1375	395	3	:	:	PUNCT
ejpam-1375	395	4	some	some	DET
ejpam-1375	395	5	simulations	simulation	NOUN
ejpam-1375	395	6	.	.	PUNCT
ejpam-1375	396	1	communications	communication	NOUN
ejpam-1375	396	2	in	in	ADP
ejpam-1375	396	3	statistics	statistic	NOUN
ejpam-1375	396	4	,	,	PUNCT
ejpam-1375	396	5	4:105–123	4:105–123	NOUN
ejpam-1375	396	6	,	,	PUNCT
ejpam-1375	396	7	1975	1975	NUM
ejpam-1375	396	8	.	.	PUNCT
ejpam-1375	397	1	[	[	X
ejpam-1375	397	2	12	12	NUM
ejpam-1375	397	3	]	]	X
ejpam-1375	397	4	m	m	VERB
ejpam-1375	397	5	kubat	kubat	NOUN
ejpam-1375	397	6	.	.	PUNCT
ejpam-1375	398	1	decision	decision	NOUN
ejpam-1375	398	2	trees	tree	NOUN
ejpam-1375	398	3	can	can	AUX
ejpam-1375	398	4	initialize	initialize	VERB
ejpam-1375	398	5	radial	radial	ADJ
ejpam-1375	398	6	basis	basis	NOUN
ejpam-1375	398	7	function	function	NOUN
ejpam-1375	398	8	networks	network	NOUN
ejpam-1375	398	9	.	.	PUNCT
ejpam-1375	399	1	transactions	transaction	NOUN
ejpam-1375	399	2	on	on	ADP
ejpam-1375	399	3	neural	neural	ADJ
ejpam-1375	399	4	networks	network	NOUN
ejpam-1375	399	5	,	,	PUNCT
ejpam-1375	399	6	9:813–821	9:813–821	NUM
ejpam-1375	399	7	,	,	PUNCT
ejpam-1375	399	8	1998	1998	NUM
ejpam-1375	399	9	.	.	PUNCT
ejpam-1375	400	1	[	[	X
ejpam-1375	400	2	13	13	NUM
ejpam-1375	400	3	]	]	PUNCT
ejpam-1375	400	4	a	a	DET
ejpam-1375	400	5	kullback	kullback	NOUN
ejpam-1375	400	6	and	and	CCONJ
ejpam-1375	400	7	r	r	NOUN
ejpam-1375	400	8	leibler	leibler	NOUN
ejpam-1375	400	9	.	.	PUNCT
ejpam-1375	401	1	on	on	ADP
ejpam-1375	401	2	information	information	NOUN
ejpam-1375	401	3	and	and	CCONJ
ejpam-1375	401	4	sufficiency	sufficiency	NOUN
ejpam-1375	401	5	.	.	PUNCT
ejpam-1375	402	1	annals	annal	NOUN
ejpam-1375	402	2	of	of	ADP
ejpam-1375	402	3	mathematical	mathematical	ADJ
ejpam-1375	402	4	statistics	statistic	NOUN
ejpam-1375	402	5	,	,	PUNCT
ejpam-1375	402	6	22:79–86	22:79–86	NUM
ejpam-1375	402	7	,	,	PUNCT
ejpam-1375	402	8	1951	1951	NUM
ejpam-1375	402	9	.	.	PUNCT
ejpam-1375	403	1	[	[	X
ejpam-1375	403	2	14	14	NUM
ejpam-1375	403	3	]	]	X
ejpam-1375	403	4	j	j	NOUN
ejpam-1375	403	5	lawless	lawless	ADJ
ejpam-1375	403	6	and	and	CCONJ
ejpam-1375	403	7	p	p	PROPN
ejpam-1375	403	8	wang	wang	PROPN
ejpam-1375	403	9	.	.	PUNCT
ejpam-1375	404	1	a	a	DET
ejpam-1375	404	2	simulation	simulation	NOUN
ejpam-1375	404	3	study	study	NOUN
ejpam-1375	404	4	if	if	SCONJ
ejpam-1375	404	5	ridge	ridge	PROPN
ejpam-1375	404	6	and	and	CCONJ
ejpam-1375	404	7	other	other	ADJ
ejpam-1375	404	8	regression	regression	NOUN
ejpam-1375	404	9	estimators	estimator	NOUN
ejpam-1375	404	10	.	.	PUNCT
ejpam-1375	405	1	communications	communication	NOUN
ejpam-1375	405	2	in	in	ADP
ejpam-1375	405	3	statistics	statistic	NOUN
ejpam-1375	405	4	,	,	PUNCT
ejpam-1375	405	5	a5:307–323	a5:307–323	PROPN
ejpam-1375	405	6	,	,	PUNCT
ejpam-1375	405	7	1975	1975	NUM
ejpam-1375	405	8	.	.	PUNCT
ejpam-1375	406	1	[	[	X
ejpam-1375	406	2	15	15	NUM
ejpam-1375	406	3	]	]	X
ejpam-1375	406	4	c	c	PROPN
ejpam-1375	406	5	lin	lin	PROPN
ejpam-1375	406	6	and	and	CCONJ
ejpam-1375	406	7	c	c	PROPN
ejpam-1375	406	8	lee	lee	PROPN
ejpam-1375	406	9	.	.	PROPN
ejpam-1375	406	10	neural	neural	ADJ
ejpam-1375	406	11	fuzzy	fuzzy	ADJ
ejpam-1375	406	12	systems	system	NOUN
ejpam-1375	406	13	;	;	PUNCT
ejpam-1375	406	14	a	a	DET
ejpam-1375	406	15	neuro	neuro	NOUN
ejpam-1375	406	16	-	-	PUNCT
ejpam-1375	406	17	fuzzy	fuzzy	ADJ
ejpam-1375	406	18	synergism	synergism	NOUN
ejpam-1375	406	19	to	to	ADP
ejpam-1375	406	20	intelligent	intelligent	ADJ
ejpam-1375	406	21	systems	system	NOUN
ejpam-1375	406	22	.	.	PUNCT
ejpam-1375	407	1	prentice	prentice	PROPN
ejpam-1375	407	2	hall	hall	PROPN
ejpam-1375	407	3	p	p	PROPN
ejpam-1375	407	4	t	t	PROPN
ejpam-1375	407	5	r	r	PROPN
ejpam-1375	407	6	,	,	PUNCT
ejpam-1375	407	7	new	new	PROPN
ejpam-1375	407	8	jersey	jersey	PROPN
ejpam-1375	407	9	,	,	PUNCT
ejpam-1375	407	10	usa	usa	PROPN
ejpam-1375	407	11	,	,	PUNCT
ejpam-1375	407	12	1996	1996	NUM
ejpam-1375	407	13	.	.	PUNCT
ejpam-1375	408	1	[	[	X
ejpam-1375	408	2	16	16	NUM
ejpam-1375	408	3	]	]	X
ejpam-1375	408	4	d	d	PROPN
ejpam-1375	408	5	mackay	mackay	PROPN
ejpam-1375	408	6	.	.	PUNCT
ejpam-1375	409	1	a	a	DET
ejpam-1375	409	2	practical	practical	ADJ
ejpam-1375	409	3	bayesian	bayesian	NOUN
ejpam-1375	409	4	framework	framework	NOUN
ejpam-1375	409	5	for	for	ADP
ejpam-1375	409	6	backpropagation	backpropagation	NOUN
ejpam-1375	409	7	networks	network	NOUN
ejpam-1375	409	8	.	.	PUNCT
ejpam-1375	410	1	neural	neural	ADJ
ejpam-1375	410	2	computation	computation	NOUN
ejpam-1375	410	3	,	,	PUNCT
ejpam-1375	410	4	4:448–472	4:448–472	PROPN
ejpam-1375	410	5	,	,	PUNCT
ejpam-1375	410	6	1992	1992	NUM
ejpam-1375	410	7	.	.	PUNCT
ejpam-1375	411	1	[	[	X
ejpam-1375	411	2	17	17	NUM
ejpam-1375	411	3	]	]	PUNCT
ejpam-1375	411	4	n	n	DET
ejpam-1375	411	5	mantel	mantel	NOUN
ejpam-1375	411	6	.	.	PUNCT
ejpam-1375	412	1	why	why	SCONJ
ejpam-1375	412	2	stepdown	stepdown	ADJ
ejpam-1375	412	3	procedures	procedure	NOUN
ejpam-1375	412	4	in	in	ADP
ejpam-1375	412	5	variables	variable	NOUN
ejpam-1375	412	6	selection	selection	NOUN
ejpam-1375	412	7	.	.	PUNCT
ejpam-1375	413	1	technometrics	technometric	NOUN
ejpam-1375	413	2	,	,	PUNCT
ejpam-1375	413	3	12:591–612	12:591–612	PROPN
ejpam-1375	413	4	,	,	PUNCT
ejpam-1375	413	5	1970	1970	NUM
ejpam-1375	413	6	.	.	PUNCT
ejpam-1375	414	1	[	[	X
ejpam-1375	414	2	18	18	NUM
ejpam-1375	414	3	]	]	PUNCT
ejpam-1375	414	4	l	l	PROPN
ejpam-1375	414	5	moses	moses	PROPN
ejpam-1375	414	6	.	.	PUNCT
ejpam-1375	415	1	think	think	VERB
ejpam-1375	415	2	and	and	CCONJ
ejpam-1375	415	3	explain	explain	VERB
ejpam-1375	415	4	with	with	ADP
ejpam-1375	415	5	statistics	statistic	NOUN
ejpam-1375	415	6	.	.	PUNCT
ejpam-1375	416	1	addison	addison	PROPN
ejpam-1375	416	2	-	-	PUNCT
ejpam-1375	416	3	wesley	wesley	PROPN
ejpam-1375	416	4	,	,	PUNCT
ejpam-1375	416	5	ma	ma	PROPN
ejpam-1375	416	6	,	,	PUNCT
ejpam-1375	416	7	1986	1986	NUM
ejpam-1375	416	8	.	.	PUNCT
ejpam-1375	417	1	[	[	X
ejpam-1375	417	2	19	19	NUM
ejpam-1375	417	3	]	]	X
ejpam-1375	417	4	m	m	PROPN
ejpam-1375	417	5	orr	orr	PROPN
ejpam-1375	417	6	.	.	PUNCT
ejpam-1375	418	1	combining	combine	VERB
ejpam-1375	418	2	regression	regression	NOUN
ejpam-1375	418	3	trees	tree	NOUN
ejpam-1375	418	4	and	and	CCONJ
ejpam-1375	418	5	rbfs	rbfs	NOUN
ejpam-1375	418	6	.	.	PUNCT
ejpam-1375	419	1	international	international	ADJ
ejpam-1375	419	2	journal	journal	PROPN
ejpam-1375	419	3	of	of	ADP
ejpam-1375	419	4	neural	neural	ADJ
ejpam-1375	419	5	systems	system	NOUN
ejpam-1375	419	6	,	,	PUNCT
ejpam-1375	419	7	10:453–465	10:453–465	NUM
ejpam-1375	419	8	,	,	PUNCT
ejpam-1375	419	9	2000	2000	NUM
ejpam-1375	419	10	.	.	PUNCT
ejpam-1375	420	1	references	reference	NOUN
ejpam-1375	420	2	486	486	NUM
ejpam-1375	420	3	[	[	SYM
ejpam-1375	420	4	20	20	NUM
ejpam-1375	420	5	]	]	PUNCT
ejpam-1375	420	6	t	t	PROPN
ejpam-1375	420	7	poggio	poggio	PROPN
ejpam-1375	420	8	and	and	CCONJ
ejpam-1375	420	9	f	f	PROPN
ejpam-1375	420	10	girosi	girosi	PROPN
ejpam-1375	420	11	.	.	PUNCT
ejpam-1375	421	1	regularization	regularization	NOUN
ejpam-1375	421	2	algorithms	algorithm	NOUN
ejpam-1375	421	3	for	for	ADP
ejpam-1375	421	4	learning	learn	VERB
ejpam-1375	421	5	that	that	PRON
ejpam-1375	421	6	are	be	AUX
ejpam-1375	421	7	equivalent	equivalent	ADJ
ejpam-1375	421	8	to	to	ADP
ejpam-1375	421	9	multilayer	multilayer	ADJ
ejpam-1375	421	10	networks	network	NOUN
ejpam-1375	421	11	.	.	PUNCT
ejpam-1375	422	1	science	science	NOUN
ejpam-1375	422	2	,	,	PUNCT
ejpam-1375	422	3	new	new	ADJ
ejpam-1375	422	4	-	-	PUNCT
ejpam-1375	422	5	series	series	NOUN
ejpam-1375	422	6	,	,	PUNCT
ejpam-1375	422	7	247:978–982	247:978–982	NUM
ejpam-1375	422	8	,	,	PUNCT
ejpam-1375	422	9	1990	1990	NUM
ejpam-1375	422	10	.	.	PUNCT
ejpam-1375	423	1	[	[	X
ejpam-1375	423	2	21	21	NUM
ejpam-1375	423	3	]	]	X
ejpam-1375	423	4	g	g	PROPN
ejpam-1375	423	5	schwartz	schwartz	PROPN
ejpam-1375	423	6	.	.	PUNCT
ejpam-1375	424	1	estimating	estimate	VERB
ejpam-1375	424	2	the	the	DET
ejpam-1375	424	3	dimension	dimension	NOUN
ejpam-1375	424	4	of	of	ADP
ejpam-1375	424	5	model	model	NOUN
ejpam-1375	424	6	.	.	PUNCT
ejpam-1375	425	1	annals	annal	NOUN
ejpam-1375	425	2	of	of	ADP
ejpam-1375	425	3	statistics	statistic	NOUN
ejpam-1375	425	4	,	,	PUNCT
ejpam-1375	425	5	6:461–464	6:461–464	PROPN
ejpam-1375	425	6	,	,	PUNCT
ejpam-1375	425	7	1978	1978	NUM
ejpam-1375	425	8	.	.	PUNCT
ejpam-1375	426	1	[	[	X
ejpam-1375	426	2	22	22	NUM
ejpam-1375	426	3	]	]	X
ejpam-1375	426	4	s	s	VERB
ejpam-1375	426	5	sclove	sclove	NOUN
ejpam-1375	426	6	.	.	PUNCT
ejpam-1375	427	1	least	least	ADJ
ejpam-1375	427	2	squares	square	NOUN
ejpam-1375	427	3	with	with	ADP
ejpam-1375	427	4	random	random	ADJ
ejpam-1375	427	5	regression	regression	NOUN
ejpam-1375	427	6	coefficient	coefficient	NOUN
ejpam-1375	427	7	.	.	PUNCT
ejpam-1375	428	1	technical	technical	ADJ
ejpam-1375	428	2	report	report	PROPN
ejpam-1375	428	3	,	,	PUNCT
ejpam-1375	428	4	department	department	NOUN
ejpam-1375	428	5	of	of	ADP
ejpam-1375	428	6	economics	economic	NOUN
ejpam-1375	428	7	,	,	PUNCT
ejpam-1375	428	8	stanford	stanford	PROPN
ejpam-1375	428	9	university	university	PROPN
ejpam-1375	428	10	,	,	PUNCT
ejpam-1375	428	11	1973	1973	NUM
ejpam-1375	428	12	.	.	PUNCT
ejpam-1375	429	1	[	[	X
ejpam-1375	429	2	23	23	NUM
ejpam-1375	429	3	]	]	PUNCT
ejpam-1375	429	4	a	a	DET
ejpam-1375	429	5	tikhonov	tikhonov	NOUN
ejpam-1375	429	6	and	and	CCONJ
ejpam-1375	429	7	v	v	ADP
ejpam-1375	429	8	arsenin	arsenin	ADJ
ejpam-1375	429	9	.	.	PUNCT
ejpam-1375	430	1	solutions	solution	NOUN
ejpam-1375	430	2	of	of	ADP
ejpam-1375	430	3	ill	ill	ADV
ejpam-1375	430	4	-	-	PUNCT
ejpam-1375	430	5	posed	pose	VERB
ejpam-1375	430	6	problems	problem	NOUN
ejpam-1375	430	7	.	.	PUNCT
ejpam-1375	431	1	wiley	wiley	PROPN
ejpam-1375	431	2	,	,	PUNCT
ejpam-1375	431	3	1977	1977	NUM
ejpam-1375	431	4	.	.	PUNCT
ejpam-1375	432	1	[	[	X
ejpam-1375	432	2	24	24	NUM
ejpam-1375	432	3	]	]	X
ejpam-1375	432	4	h	h	NOUN
ejpam-1375	432	5	white	white	PROPN
ejpam-1375	432	6	.	.	PUNCT
ejpam-1375	433	1	maximum	maximum	ADJ
ejpam-1375	433	2	likelihood	likelihood	NOUN
ejpam-1375	433	3	estimation	estimation	NOUN
ejpam-1375	433	4	of	of	ADP
ejpam-1375	433	5	misspecified	misspecifie	VERB
ejpam-1375	433	6	models	model	NOUN
ejpam-1375	433	7	.	.	PUNCT
ejpam-1375	434	1	econometrica	econometrica	PROPN
ejpam-1375	434	2	,	,	PUNCT
ejpam-1375	434	3	50:1	50:1	NUM
ejpam-1375	434	4	–	–	PUNCT
ejpam-1375	434	5	25	25	NUM
ejpam-1375	434	6	,	,	PUNCT
ejpam-1375	434	7	1982	1982	NUM
ejpam-1375	434	8	.	.	PUNCT
ejpam-1375	435	1	[	[	X
ejpam-1375	435	2	25	25	NUM
ejpam-1375	435	3	]	]	X
ejpam-1375	435	4	l	l	PROPN
ejpam-1375	435	5	wilkinson	wilkinson	PROPN
ejpam-1375	435	6	.	.	PUNCT
ejpam-1375	435	7	systat	systat	PROPN
ejpam-1375	435	8	:	:	PUNCT
ejpam-1375	435	9	the	the	DET
ejpam-1375	435	10	system	system	NOUN
ejpam-1375	435	11	for	for	ADP
ejpam-1375	435	12	statistics	statistic	NOUN
ejpam-1375	435	13	.	.	PUNCT
ejpam-1375	436	1	systat	systat	PROPN
ejpam-1375	436	2	,	,	PUNCT
ejpam-1375	436	3	evanston	evanston	PROPN
ejpam-1375	436	4	,	,	PUNCT
ejpam-1375	436	5	il	il	PROPN
ejpam-1375	436	6	,	,	PUNCT
ejpam-1375	436	7	1989	1989	NUM
ejpam-1375	436	8	.	.	PUNCT