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

Udomboso, C. G.Amahia, G. N.Dontwi, I. K.2021-05-252021-05-2520161538-9472ui_art_udomboso_adjusted_2016Journal of Modern Applied Statistical Methods 15(2), pp. 411-427http://ir.library.ui.edu.ng/handle/123456789/5337In 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.enStatistical neural networkNetwork information criterionNetwork information criterionAdjusted network information criterionTransfer functionAn adjusted network information criterion for model selection in statistical neural network modelsArticle