Efficient training of RBF networks for classification


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.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Volume 1 ISSN - 0537-9989
Event Title: 9th International Conference on Artificial Neural Networks
Event Type: Other
Event Dates: 1999-09-07 - 1999-09-07
Uncontrolled Keywords: Radial Basis,regression,Multi-layer Perceptrons,probabilities,logistic,softmax outputs,Generalised Linear Models,non-linear optimisation,datasets
Last Modified: 27 Jun 2024 11:58
Date Deposited: 14 Sep 2009 16:02
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... &isnumber=17760 (Publisher URL)
PURE Output Type: Paper
Published Date: 1999
Authors: Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Published Version

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