Learning SVM Kernel Function with Genetic Programming

Abstract

SVM (Support Vector Machine) proposed by Vladimir Vapnik is one of the most widely used learning algoirithm, which uses kernel function to create non-linear classifiers. Kernel functions can be constructed from simple kernel by applying some constructing rules. We therefore propose a method in which genetic programming searches better kernel for solving a problem.