The University of Southampton
University of Southampton Institutional Repository

Robust maximum likelihood training of heteroscedastic probabilistic neural networks

Robust maximum likelihood training of heteroscedastic probabilistic neural networks
Robust maximum likelihood training of heteroscedastic probabilistic neural networks
We consider the probabilistic neural network (PNN) that is a mixture of Gaussian basis functions having different variances. Such a Gaussian heteroscedastic PNN is more economic, in terms of the number of kernel functions required, than the Gaussian mixture PNN of a common variance. The expectation-maximisation (EM) algorithm, although a powerful technique for constructing maximum likelihood (ML) homoscedastic PNNs, often encounters numerical difficulties when training heteroscedastic PNNs. We combine a robust statistical technique known as the Jack-knife with the EM algorithm to provide a robust ML training algorithm. An artificial-data case, the two-dimensional XOR problem, and a real-data case, success or failure prediction of UK private construction companies, are used to evaluate the performance of this robust learning algorithm.
739-747
Yang, Z.R.
3cf3b5d7-a256-4251-a358-1e51b5a44214
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Yang, Z.R.
3cf3b5d7-a256-4251-a358-1e51b5a44214
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80

Yang, Z.R. and Chen, S. (1998) Robust maximum likelihood training of heteroscedastic probabilistic neural networks. Neural Networks, 11 (4), 739-747. (doi:10.1016/S0893-6080(98)00024-0).

Record type: Article

Abstract

We consider the probabilistic neural network (PNN) that is a mixture of Gaussian basis functions having different variances. Such a Gaussian heteroscedastic PNN is more economic, in terms of the number of kernel functions required, than the Gaussian mixture PNN of a common variance. The expectation-maximisation (EM) algorithm, although a powerful technique for constructing maximum likelihood (ML) homoscedastic PNNs, often encounters numerical difficulties when training heteroscedastic PNNs. We combine a robust statistical technique known as the Jack-knife with the EM algorithm to provide a robust ML training algorithm. An artificial-data case, the two-dimensional XOR problem, and a real-data case, success or failure prediction of UK private construction companies, are used to evaluate the performance of this robust learning algorithm.

Text
nn-els98.pdf - Other
Download (252kB)

More information

Published date: June 1998
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251026
URI: http://eprints.soton.ac.uk/id/eprint/251026
PURE UUID: 9cd8ec79-2bb3-4e38-b7a7-05ee2a57ce3b

Catalogue record

Date deposited: 31 Mar 2000
Last modified: 31 Jan 2022 17:44

Export record

Altmetrics

Contributors

Author: Z.R. Yang
Author: S. Chen

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×