A continuous wavelet kernel for support vector regression

A new kernel function is proposed for support vector regression(SVR). An one-to-one mapping is adopted for dimensionality reduction and then continuous wavelet transform is utilized to construct the nonlinear mapping phi(x) from the input space S to the feature space. So we call it continuous wavelet kernel function(CWKF). This wavelet kernel is not translation invariant kernel, instead inner product kernel and need not parameter selecting. The quadratic program of support vector regression has feasible solution if we use CWKF. Numerical experiments demonstrate the effectiveness of this method.