Empirical evaluation of Bayesian sampling for neural classifiers

Husmeier, D. , Penny, W.D. and Roberts, S.J. (1998) Empirical evaluation of Bayesian sampling for neural classifiers. In: Niklasson, L., Boden, M. and Ziemke, T. (eds.) Proceedings of the 8th International Conference on Artificial Neural Networks. Series: Perspectives in neural computing. Springer: London, UK, pp. 323-328. ISBN 9783540762638

Abstract

Adopting a Bayesian approach and sampling the network parameters from their posterior distribution is a rather novel and promising method for improving the generalisation performance of neural network predictors. The present empirical study applies this scheme to a set of different synthetic and real-world classification problems. The paper focuses on the dependence of the prediction results on the prior distribution of the network parameters and hyperparameters, and provides a critical evaluation of the automatic relevance determination (ARD) scheme for detecting irrelevant inputs.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Husmeier, Professor Dirk
• Authors: Husmeier, D., Penny, W.D., and Roberts, S.J.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: Springer
• ISBN: 9783540762638