The Genetic Kernel Support Vector Machine: Description and Evaluation (2005)
| Venue: | Artificial Intelligence Review |
| Citations: | 18 - 0 self |
BibTeX
@ARTICLE{Howley05thegenetic,
author = {Tom Howley and Michael G. Madden},
title = {The Genetic Kernel Support Vector Machine: Description and Evaluation},
journal = {Artificial Intelligence Review},
year = {2005},
volume = {24},
pages = {379--395}
}
OpenURL
Abstract
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.







