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dc.contributor.authorMadden, Michael G.en
dc.contributor.authorHowley, Tomen
dc.date.accessioned2009-05-13T09:31:52Zen
dc.date.available2009-05-13T09:31:52Zen
dc.date.issued2004en
dc.identifier.citation"The Genetic Evolution of Kernels for Support Vector Machine Classifiers" , Tom Howley and Michael G. Madden. Proceedings of AICS-2004, 15th Irish Conference on Artificial Intelligence & Cognitive Science, September 2004.en
dc.identifier.urihttp://hdl.handle.net/10379/185en
dc.description.abstractAbstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classi¿cation of data. One problem that faces the user of an SVM is how to choose a kernel and the speci¿c parameters for that kernel.Applications of an SVM therefore require a search for the optimum settings for aparticular problem. This paper proposes a classi¿cation technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve akernel for a SVMclassi¿er. Results of initial experiments with the proposed tech-nique are presented. These results are compared with those of a standard SVM classi¿er using the Polynomial or RBF kernel with various parameter settings.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.subjectSupport vector machinesen
dc.subjectGenetic kernel (GK SVM)en
dc.subjectGenetic programmingen
dc.subjectSVM classifieren
dc.subjectPolynomial kernelen
dc.subject.lcshSupport vector machinesen
dc.subject.lcshKernel functionsen
dc.subject.lcshGenetic programming (Computer science)en
dc.subject.lcshPolynomialsen
dc.titleThe Genetic Evolution of Kernels for Support Vector Machine Classifiersen
dc.typeConference Paperen
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