Bayesian methods for neural networks

Christopher M. Bishop

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.
    Original languageEnglish
    Title of host publicationOxford Lectures on Neural Networks
    EditorsL. Tarassenko, E. Rolls, D. Sherrington
    Place of PublicationOxford
    PublisherOxford University Press
    Publication statusPublished - 1995

    Keywords

    • bayesian
    • neural networks
    • learning
    • pattern recognition

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