Morgan, Peter Huw ORCID: https://orcid.org/0000-0002-8555-3493, Curry, Bruce and Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X 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 |
Official URL: http://dx.doi.org/10.1051/ejess:2000104
Abstract
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 |
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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 |
URI: | https://orca.cardiff.ac.uk/id/eprint/37986 |
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