An adjusted network information criterion for model selection in statistical neural network models

Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/5337

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DC Field	Value	Language
dc.contributor.author	Udomboso, C. G.	-
dc.contributor.author	Amahia, G. N.	-
dc.contributor.author	Dontwi, I. K.	-
dc.date.accessioned	2021-05-25T10:53:41Z	-
dc.date.available	2021-05-25T10:53:41Z	-
dc.date.issued	2016	-
dc.identifier.issn	1538-9472	-
dc.identifier.other	ui_art_udomboso_adjusted_2016	-
dc.identifier.other	Journal of Modern Applied Statistical Methods 15(2), pp. 411-427	-
dc.identifier.uri	http://ir.library.ui.edu.ng/handle/123456789/5337	-
dc.description.abstract	In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC.	en_US
dc.language.iso	en	en_US
dc.publisher	JMASM, Inc.	en_US
dc.subject	Statistical neural network	en_US
dc.subject	Network information criterion	en_US
dc.subject	Network information criterion	en_US
dc.subject	Adjusted network information criterion	en_US
dc.subject	Transfer function	en_US
dc.title	An adjusted network information criterion for model selection in statistical neural network models	en_US
dc.type	Article	en_US
Appears in Collections:	Scholarly works

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