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SUPERVISED AND UNSUPERVISED LEARNING IN RADIAL BASIS FUNCTION CLASSIFIERS

Abstract:

The paper considers a number of strategies for training radial basis function (RBF) classifiers. A benchmark problem is constructed using ten-dimensional input patterns which have to be classified into one of three classes. The RBF networks are trained using a two-phase approach (unsupervised clustering for the first layer followed by supervised learning for the second layer), error backpropagation (supervised learning for both layers) and a hybrid approach. It is shown that RBF classifiers t...

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Publication status:
Published

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Publisher copy:
10.1049/ip-vis:19941324

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science, Biomedical Research Centre
Role:
Author
Publisher:
IEE
Volume:
141
Issue:
4
Pages:
210-216
Publication date:
1994-08-05
DOI:
ISSN:
1350-245X
URN:
uuid:cfb5bdaa-c6f7-4513-886b-c43f3e2287b3
Source identifiers:
61744
Local pid:
pubs:61744

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