Efficient training of RBF networks for classification

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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.



Original languageEnglish
Number of pages6
Publication statusPublished - 1999
Event9th International Conference on Artificial Neural Networks - Edinburgh, United Kingdom
Duration: 7 Sep 19997 Sep 1999


Conference9th International Conference on Artificial Neural Networks
Abbreviated titleICANN 99
CountryUnited Kingdom

Bibliographic note

Volume 1 ISSN - 0537-9989


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

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