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

Husmeier, D, Penny, W D ORCID: https://orcid.org/0000-0001-9064-1191 and Roberts, S J (1999) An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks, 12 (4-5). pp. 677-705. ISSN 0893-6080

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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-called automatic relevance determination (ARD) method. The article concludes with a comparison of the achieved classification results with those obtained with (i) the evidence scheme and (ii) with non-Bayesian methods.

Item Type:	Article
Faculty \ School:	Faculty of Social Sciences > School of Psychology
UEA Research Groups:	Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences
Depositing User:	Pure Connector
Date Deposited:	23 Aug 2017 05:04
Last Modified:	19 Apr 2023 22:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/64636
DOI:	10.1016/S0893-6080(99)00020-9

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