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Title: An adjusted network information criterion for model selection in statistical neural network models
Authors: Udomboso, C. G.
Amahia, G. N.
Dontwi, I. K.
Keywords: Statistical neural network
Network information criterion
Network information criterion
Adjusted network information criterion
Transfer function
Issue Date: 2016
Publisher: JMASM, Inc.
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.
ISSN: 1538-9472
Appears in Collections:Scholarly works

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