A SVM function approximation approach with good performances in interpolation and extrapolation

Function approximation estimation and prediction are used widely in many fields such as control and signal processing. The merit and shortcoming of existing methods of function approximation and regression are analyzed, and a new function approximation and regression approach which is based on the combination of SVMs (support vector machines) is presented. The new approach fully exerts the merit of SVM, and overcomes the shortcoming in extrapolation of function approximation and regression. The experiment demonstrates that the new approach improves the precision of SVM function approximation greatly in both interpolation and extrapolation.