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A New Compound Kernel Function for SVM

Support Vector Machines (SVM) is one of most important algorithm in machine learning area. The choice of kernel function can have great influence on classification and approximation ability. Choosing appropriate kernel function and weight parameters is one of the keys to utilize SVM. Single kernel function always has its limitation in the application. We propose a new kernel function based on the analysis about the constitute conditions of the kernel function and the characteristics of different kinds of kernel function-linear compound kernel function, this function not only can reduce the amount of parameters of the kernel function, but also has good learning ability and generalizing ability. And we have tested the effectiveness of the kernel function through simulation.