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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.

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Published date: June 1998
Organisations: Southampton Wireless Group

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

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Date deposited: 31 Mar 2000
Last modified: 14 Mar 2024 05:07

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

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