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Pruning neural networks by minimization of the estimated variance

Morgan, Peter Huw, Curry, Bruce and Beynon, Malcolm James 2000. Pruning neural networks by minimization of the estimated variance. European Journal of Economic and Social Systems 14 (1) , pp. 1-16. 10.1051/ejess:2000104

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This paper presents a series of results on a method of pruning neural networks. An approximation to the estimated variance of errors, V, is constructed containing a supplementary parameter, a - the estimated variance itself being the limit of the function, V, as a tends to zero. The network weights are fitted using a minimization algorithm with V as objective function. The parameter, a, is reduced successively in the course of fitting. Results are presented using synthetic functions and the well-known airline passenger data. We find, for example, that the network can discover, in the course of being pruned, evidence of redundancy in the variables.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics
Uncontrolled Keywords: Neural network, pruning, generalization, penalty function, estimated variance
Publisher: EDP Sciences
ISSN: 1292-8895
Last Modified: 04 Jun 2017 04:23

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