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Accession Number:
ADP007170
Title:
Generalization through Minimal Networks with Application to Forecasting,
Corporate Author:
STANFORD UNIV CA
Report Date:
1992-01-01
Abstract:
Inspired by the information theoretic idea of minimum description length, we add a term to the usual back-propagation cost function that penalizes network complexity. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. This method, called weight-elimination, is contrasted to ridge regression and to cross-validation. We apply weight-elimination to time series prediction. On the sunspot series, the network outperforms traditional statistical approaches and shows the same predictive power as multivariate adaptive regression splines.
Supplementary Note:
This article is from 'Computing Science and Statistics: Proceedings of the Symposium on the Interface Critical Applications of Scientific Computing: Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p362-370.
Pages:
0009
File Size:
0.00MB
