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Support vector regression using a hybrid wavelet support vector kernel is presented in this paper. A hybrid wavelet kernel construction for support vector machine is introduced. The construction involves a multi-dimensional sinc wavelet function together with one of the conventional kernel functions. We show that the hybrid kernel is an admissible support vector (SV) kernel. The hybrid kernels thus constructed are used for the function approximation problem. The experimental results show that the hybrid kernels provide better function approximation in the mapped feature space compared to conventional kernels.