Please use this identifier to cite or link to this item:
http://ir.library.ui.edu.ng/handle/123456789/5337
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. |
URI: | http://ir.library.ui.edu.ng/handle/123456789/5337 |
ISSN: | 1538-9472 |
Appears in Collections: | Scholarly works |
Files in This Item:
File | Description | Size | Format | |
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(23) ui_art_udomboso_adjusted_2016.pdf | 1.68 MB | Adobe PDF | View/Open |
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