Sinc-Cauchy hybrid wavelet kernel for Support Vector Machines

Abstract

Support vector machine (SVM) is a machine-learning algorithm, which learns to perform the classification task through a supervised learning procedure, based on pre-classified data examples. Support vector classification using a Sinc-Cauchy hybrid wavelet kernel is presented in this paper. A hybrid wavelet kernel construction for support vector machine is introduced. The construction involves a multi-dimensional sinc wavelet function together with Cauchy kernel. We show that the hybrid kernel is an admissible kernel. Hybrid kernels provide better classification of the signal points in the mapped feature space. The Sinc-Cauchy hybrid kernel thus constructed is used for the classification of cardiac single photon emission computed tomography (SPECT) images and cardiac arrhythmia signals. The experimental results show that promising generalization performance can be achieved with the hybrid kernel, compared to conventional kernels.