Least squares support vector machine based on continuous wavelet kernel

Authors:
Xiangjun Wen;Yunze Cai;Xiaoming Xu
Affiliations:
Automation Department, Shanghai Jiaotong University, Shanghai, China;Automation Department, Shanghai Jiaotong University, Shanghai, China;Automation Department, Shanghai Jiaotong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

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.