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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag London Limited
About this chapter
Cite this chapter
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
Download citation
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