Artificial Intelligence Review

, Volume 24, Issue 3–4, pp 379–395 | Cite as

The Genetic Kernel Support Vector Machine: Description and Evaluation



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


classification genetic Kernel SVM genetic programming Mercer Kernel model selection support vector machine 


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Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Department of Information TechnologyNational University of IrelandGalwayIreland

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