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The Genetic Evolution of Kernels for Support Vector Machine Classifiers

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dc.contributor.author Madden, Michael G. en
dc.contributor.author Howley, Tom en
dc.date.accessioned 2009-05-13T09:31:52Z en
dc.date.available 2009-05-13T09:31:52Z en
dc.date.issued 2004 en
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.uri http://hdl.handle.net/10379/185 en
dc.description.abstract Abstract. 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.format application/pdf en
dc.language.iso en en
dc.subject Support vector machines en
dc.subject Genetic kernel (GK SVM) en
dc.subject Genetic programming en
dc.subject SVM classifier en
dc.subject Polynomial kernel en
dc.subject.lcsh Support vector machines en
dc.subject.lcsh Kernel functions en
dc.subject.lcsh Genetic programming (Computer science) en
dc.subject.lcsh Polynomials en
dc.title The Genetic Evolution of Kernels for Support Vector Machine Classifiers en
dc.type Conference Paper en

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