Free Access
Issue |
E.J.E.S.S.
Volume 14, Number 1, 2000
Neural Models in Economy and Management Science
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Page(s) | 1 - 16 | |
DOI | https://doi.org/10.1051/ejess:2000104 |
DOI: 10.1051/ejess:2000104
European Journal of Economic and Social Systems 14 N 1 (2000) 1-16
Pruning neural networks by minimization of the estimated variance
Peter Morgan, Bruce Curry and Malcom Beynon
Cardiff Business School, Aberconway Building, Colum Drive, Cardiff, CF1 3EU, Wales, UK
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.
Keywords: Neural network, pruning, generalization, penalty function,
estimated variance.
Copyright EDP Sciences 2000