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.

• Type: Journal
• Year: 2005
• Where: AIR
• Authors: Tom Howley, Michael G. Madden
• Keywords: AIR 2005, Genetic Kernel SVM, GK SVM, SVM Classifier