Multi-scale Least Square Wavelet Support Vector Machine

Original Least square Wavelet Support Vector Machine (LSSVM) algorithm can not reach desired precision in multi-scale regression. To solve the problem, a multi-scale wavelet LSSVM algorithm is proposed in this paper by using a wavelet kernel. Mexican-hat wavelet function is used as the support vector kernel function, and further the Least square Wavelet Support Vector Machine (LS-WSVM) algorithm is presented. On this basis, the global optimum of the multi-scale regression modeling problem can be obtained by solving a quadratic programming problem. As a result, the regression model can effectively approximate multi-scale signals. Therefore, LS-WSVM is an efficient modeling method and worth popularization and application by computer simulation results.