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...
المؤلفون الرئيسيون: | Tarassenko, L, Roberts, S |
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التنسيق: | Conference item |
منشور في: |
IEE
1994
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مواد مشابهة
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