Efficient training of RBF networks for classification

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Abstract

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

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Original languageEnglish
Pages210-215
Number of pages6
StatePublished - 1999
Event9th International Conference on Artificial Neural Networks - Edinburgh, United Kingdom

Conference

Conference9th International Conference on Artificial Neural Networks
Abbreviated titleICANN 99
CountryUnited Kingdom
CityEdinburgh
Period7/09/997/09/99

Bibliographic note

Volume 1 ISSN - 0537-9989

    Keywords

  • Radial Basis, regression, Multi-layer Perceptrons, probabilities, logistic, softmax outputs, Generalised Linear Models, non-linear optimisation, datasets

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