The Bayesian paradigm: second generation neural computing

Penny, W.D., Husmeier, D. and Roberts, S.J. (2000) The Bayesian paradigm: second generation neural computing. In: Lisboa, P.J.G., Ifeachor, E.C. and Srczepaniak, A.S. (eds.) Artificial Neural Networks in Biomedicine. Series: Perspectives in neural computing. Springer-Verlag: London, UK, pp. 11-23. ISBN 9781852330057 (doi: 10.1007/978-1-4471-0487-2_2)

Abstract

When reasoning in the presence of uncertainty there is a unique and self-consistent set of rules for induction and model selection – Bayesian inference. Recent advances in neural networks have been fuelled by the adoption of this Bayesian framework, either implicitly, for example through the use of committees, or explicitly through Bayesian evidence and sampling frameworks. In this chapter, we show how this ‘second generation’ of neural network techniques can be applied to biomedical data and focus on the networks’ ability to provide assessments of the confidence associated with its predictions. This is an essential requirement for any automatic biomedical pattern recognition system. It allows low confidence decisions to be highlighted and deferred, possibly to a human expert, and falls naturally out of the Bayesian framework.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Husmeier, Professor Dirk
• Authors: Penny, W.D., Husmeier, D., and Roberts, S.J.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: Springer-Verlag
• ISBN: 9781852330057