Twin Support Vector Machines Based on the Mixed Kernel function

Abstract

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certain extent, this will limit the performance of TWSVM. In order to solve the problem of selecting kernel functions in TWSVM, we propose the twin support vector machines based on the mixed kernel function (MK-TWSVM) in this paper. To make full use of the learning ability of local kernel functions and the excellent generalization ability of global kernel functions, MK-TWSVM selects a global kernel function and a local kernel function to construct a mixed kernel function which has the better performance. The experimental results indicate that the mixed kernel function makes TWSVM have the good learning ability and generalization ability. So it improves the performance of TWSVM.