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

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dc.contributor.authorUdomboso, C. G.
dc.contributor.authorAmahia, G. N.
dc.contributor.authorDontwi, I. K.
dc.date.accessioned2021-05-25T10:53:41Z
dc.date.available2021-05-25T10:53:41Z
dc.date.issued2016
dc.description.abstractIn 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.identifier.issn1538-9472
dc.identifier.otherui_art_udomboso_adjusted_2016
dc.identifier.otherJournal of Modern Applied Statistical Methods 15(2), pp. 411-427
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/5337
dc.language.isoenen_US
dc.publisherJMASM, Inc.en_US
dc.subjectStatistical neural networken_US
dc.subjectNetwork information criterionen_US
dc.subjectNetwork information criterionen_US
dc.subjectAdjusted network information criterionen_US
dc.subjectTransfer functionen_US
dc.titleAn adjusted network information criterion for model selection in statistical neural network modelsen_US
dc.typeArticleen_US

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