Abstract
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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Howley, T., Madden, M.G. The Genetic Kernel Support Vector Machine: Description and Evaluation. Artif Intell Rev 24, 379–395 (2005). https://doi.org/10.1007/s10462-005-9009-3
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DOI: https://doi.org/10.1007/s10462-005-9009-3