An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers

Abstract

This article gives a concise overview of Bayesian sampling for neural networks, and then presents an extensive evaluation on a set of various benchmark classification problems. The main objective is to study the sensitivity of this scheme to changes in the prior distribution of the parameters and hyperparameters, and to evaluate the efficiency of the so… (More)
DOI: 10.1016/S0893-6080(99)00020-9

Topics

Cite this paper

@article{Husmeier1999AnEE, title={An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers}, author={Dirk Husmeier and William D. Penny and Stephen J. Roberts}, journal={Neural networks : the official journal of the International Neural Network Society}, year={1999}, volume={12 4-5}, pages={677-705} }