Abstract
SVM (Support Vector Machines) is the most advanced machine learning algorithm in the field of pattern recognition. The selection of kernel functions will have a direct impact on the performance of SVM. This paper analyzed Linear kernel function, Polynomial kernel function, Radial basis function (RBF), Sigmoid kernel function, Fourier kernel function, B-spline kernel function and Wavelet kernel function, seven types of common kernel functions, and it adopted a new kernel function-compound kernel function. The novel kernel function combines three types of common kernel functions and has better generalization ability and better learning ability. Experimental results show the superiority of the compound kernel function.
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