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The Genetic Evolution of Kernels for Support Vector Machine Classifiers

Madden, Michael G.
; 
Howley, Tom
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2004
Keywords
Support vector machines, Genetic kernel (GK SVM), Genetic programming, SVM classifier, Polynomial kernel
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Conference Paper
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"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.
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.
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Information Technology (Conference Papers)