Sciweavers

Share
AIR
2005

The Genetic Kernel Support Vector Machine: Description and Evaluation

9 years 2 months ago
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.
Tom Howley, Michael G. Madden
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where AIR
Authors Tom Howley, Michael G. Madden
Comments (0)
books