The genetic kernel support vector machine: description and evaluation
Madden, Michael G.
MetadataShow full item record
This item's downloads: 0 (view details)
Howley, Tom; Madden, Michael G. (2005). The genetic kernel support vector machine: description and evaluation. Artificial Intelligence Review 24 (3), 379-395
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.