Research on smoothing support vector regression based on cubic spline interpolation

Smoothing functions can transform the unsmooth support vector regression into smooth ones, and thus better regression results are generated. In the paper, using Cubic Spline Interpolation, a new polynomial smoothing function is proposed for the vertical bar x vertical bar(2)(6)function in epsilon-insensitive support vector regressions. Theoretical analysis shows that S-epsilon(2)-function is better than p(epsilon)(2)-function in properties, and the approximation accuracy of the proposed smoothing function is two order of magnitude higher than that of the P-epsilon(2)-function. The experimental results show the validity of the model. Therefore, the new better polynomial smooth functions is provided for smoothing support vector regression and related areas.