Least Squares Support Vector Machine Based on Continuous Wavelet Kernel

摘要

Based on the continuous wavelet transform theory and conditions of the admissible support vector kernel, a novel notion of multidimensional wavelet kernels is proposed for Least Squares Support Vector Machine (LS-WSVM) for pattern recognition and function estimation. Theoretic analysis of the wavelet kernel is discussed in detail. The good approximation property of wavelet kernel function enhances the generalization ability of LS-WSVM method and some experimental results are presented to illustrate the effectiveness and feasibility of the proposed method.