GPES: An algorithm for evolving hybrid kernel functions of Support Vector Machines
Tanasanee Phienthrakul, Boonserm Kijsirikul · 2007
The support vector machine (SVM) is a popular approach to the classification of data. One problem of SVM is how to choose a kernel and the parameters for the kernel. This paper proposes a classification technique, called GPES, that combines genetic programming (GP) and evolutionary strategies (ES) to evolve a hybrid kernel for an SVM classifier. The hybrid kernels are represented as trees that have some adjustable parameters. These hybrid kernels are also the Mercer's kernels. The experimental results are compared with a standard SVM classifier using the polynomial and radial basis function kernels with various parameter settings.