Facilitating the applications of support vector machine by using a new kernel

In the last few years, the applications of support vector machine (SVM) have substantially increased due to the high generalization performance and modeling of non-linear relationships. However, whether SVM behaves well largely depends on its adopted kernel function. The most commonly used kernels include linear, polynomial inner product functions and the Radial Basis Function (RBF), etc. Since the nature of the data is usually unknown, it is very difficult to make, on beforehand, a proper choice from the mentioned kernels. Usually, more than one kernel are applied to select the one which gives the best prediction performance but with a very time-consuming optimization procedure. This paper presents a kernel function based on Lorentzian function which is well-known in the field of statistics. The presented kernel can properly deal with a large variety of mapping problems due to its flexibility to vary. The applicability, suitability, performance and robustness of the presented kernel are investigated on bi-spiral benchmark data set as well as seven data sets from the UCI benchmark repository. The experiment results demonstrate that the presented kernel is robust and has stronger mapping ability comparing with the standard kernel functions, and it can obtain better generalization performance. In general, the proposed kernel can be served as a generic alternative for the common linear, polynomial and RBF kernels.