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Evaluation of Neural Network Models

  • Radford M. Neal
Part of the Lecture Notes in Statistics book series (LNS, volume 118)

Abstract

This chapter reports empirical evaluations of the predictive performance of Bayesian neural network models applied to several synthetic and real data sets. Good results were obtained when large networks with appropriate priors were used on small data sets for a synthetic regression problem, confirming expectations based on properties of the associated priors over functions. The Automatic Relevance Determination model was effective in suppressing irrelevant inputs in tests on synthetic regression and classification problems. Tests on two real data sets showed that Bayesian neural network models, implemented using hybrid Monte Carlo, can produce good results when applied to realistic problems of moderate size.

Keywords

Hide Layer Neural Network Model Hide Unit Irrelevant Attribute Bayesian Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Radford M. Neal
    • 1
  1. 1.Department of Statistics and Department of Computer ScienceUniversity of TorontoTorontoCanada

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