✦ LIBER ✦

On the training of radial basis function classifiers

✍ Scribed by M.T. Musavi; W. Ahmed; K.H. Chan; K.B. Faris; D.M. Hummels

Publisher: Elsevier Science
Year: 1992
Tongue: English
Weight: 671 KB
Volume: 5
Category: Article
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(05)80038-3

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✦ Synopsis

An approach./or the implenwntation ql'the Radial Basis Function (RBF) technique is presented and applied to a network qlthe appropriate architecture. TIw paper evplores a melhodohlgy /br selecting kernel fimction parameters and tlw t:~tent to which the mtmber ~?/ RBF mules can be reduced without significantly q/feeling the overall training error These objectit'es are a~z'omplished through algorithms that shall be described ill detail. Emphasis is also placed on the problems faced h.l' a technique that has been proved stq~erior to the more traditional training algorithms, part iczthlr/)' ill terms o[processing.vJeed and soh,ahility q/'nonlinear patterns. Solutions are consequentl.l' proposed ill view o/making RBF a mrnv e[licient melhod./br interpolation and class(/icalion purposes.

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