Least Squares Support Vector Machine on Gaussian Wavelet Kernel Function Set
The kernel function of support vector machine (SVM) is an important factor for the learning result of SVM. Based on the wavelet decomposition and conditions of the support vector kernel function, Gaussian wavelet kernel function set for SVM is proposed. Each one of these kernel functions is a kind of orthonormal function, and it can simulate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. According to the wavelet kernel function and the regularization theory, Least squares support vector machine on Gaussian wavelet kernel function set (LS-GWSVM) is proposed to greatly simplify the solving process of GWSVM. The LS-GWSVM is then applied to the regression analysis and classifying. Experiment results show that the regression’s precision is improved by LS-GWSVM, compared with LS-SVM whose kernel function is Gaussian function.
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