Artificial Intelligence Review

, Volume 24, Issue 3, pp 379–395

The Genetic Kernel Support Vector Machine: Description and Evaluation

Article

DOI: 10.1007/s10462-005-9009-3

Cite this article as:
Howley, T. & Madden, M.G. Artif Intell Rev (2005) 24: 379. doi:10.1007/s10462-005-9009-3

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

Keywords

classificationgenetic Kernel SVMgenetic programmingMercer Kernelmodel selectionsupport vector machine

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Department of Information TechnologyNational University of IrelandGalwayIreland