Abstract The kernel function of support vector machine (SVM) is an important factor to the learning result of SVM. Based on the wavelet decomposition and conditions of the support vector kernel function, a new scaling kernel function for SVM (SSVM) is proposed. This function is not only a kind of horizontal floating orthonormal function, but also can simulate any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. According to the scaling kernel function and the regularization theory, a least squares support vector machine on scaling kernel function (LSSSVM) is proposed to simplify the solving process of SSVM. The LSSSVM is then applied to the regression analysis and classification. Experimental results show that the precision of regression is improved, compared with LSSVM whose kernel function is Gauss function.
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2006, Vol. 19 
