A multiresolution wavelet kernel for support vector regression

Abstract

In this paper a multiresolution wavelet kernel function (MWKF) is proposed for support vector regression. It is different from traditional SVR that the process of reducing dimension is utilized before increasing dimension. The nonlinear mapping Φ(x) from the input space 5 to the feature space has explicit expression based on dimensionality reduction and wavelet multiresolution analysis. This wavelet kernel function can be represented by inner product. This method guarantee that quadratic program of support vector regression has feasible solution and need not parameter selecting in kernel function. Numerical experiments demonstrate the effectiveness of this method. © Springer-Verlag Berlin Heidelberg 2006.