Free Access
Volume 14, Number 1, 2000
Neural Models in Economy and Management Science
Page(s) 1 - 16
DOI: 10.1051/ejess:2000104

European Journal of Economic and Social Systems 14 N$^\circ$ 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


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

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.