SUPERVISED AND UNSUPERVISED LEARNING IN RADIAL BASIS FUNCTION CLASSIFIERS

SUPERVISED AND UNSUPERVISED LEARNING IN RADIAL BASIS FUNCTION CLASSIFIERS

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 cluste...

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Dades bibliogràfiques
Autors principals:	Tarassenko, L, Roberts, S
Format:	Conference item
Publicat:	IEE 1994

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