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Pruning neural networks by minimization of the estimated variance

Morgan, Peter Huw ORCID:, Curry, Bruce and Beynon, Malcolm James ORCID: 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: 21 Oct 2022 09:50

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