Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 24, Issue 3-4, Pages 379-395Publisher
SPRINGER
DOI: 10.1007/s10462-005-9009-3
Keywords
classification; genetic Kernel SVM; genetic programming; Mercer Kernel; model selection; support vector machine
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The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings.
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