Least-squares auto-correlation wavelet kernel method for regression estimation

Based on the auto-correlation wavelet kernel, a novel notion of least squares support vector machine (LS-SVM) with universal auto-correlation wavelet kernels is proposed for approximating arbitrary nonlinear functions. The translation invariant property of the auto-correlation wavelet kernel function enhances the generalization ability of the LS-SVM method and some experimental results are included to demonstrate the efficiency and validity of the proposed method.