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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

A regularisation scheme is derived from a simple Bayesian approach, where the maximum likelihood estimate of the network parameters is replaced by the mode of their posterior distribution. Conjugate priors for the various network parameters are introduced, which give rise to regularisation terms that can be viewed as a generalisation of simple weight decay. It is shown how the posterior mode can be found with a slightly modified version of the EM algorithm.

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© 1999 Springer-Verlag London Limited

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Husmeier, D. (1999). A simple Bayesian regularisation scheme. In: Neural Networks for Conditional Probability Estimation. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0847-4_9

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  • DOI: https://doi.org/10.1007/978-1-4471-0847-4_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-095-8

  • Online ISBN: 978-1-4471-0847-4

  • eBook Packages: Springer Book Archive

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