Least-Squares wavelet kernel method for regression estimation

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

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Abstract

Based on the wavelet decomposition and reproducing kernel Hilbert space (RKHS), a novel notion of least squares wavelet support vector machine (LS-WSVM) with universal reproducing wavelet kernels is proposed for approximating arbitrary nonlinear functions. The good reproducing property of wavelet kernel function enhances the generalization ability of LS-WSVM method and some experimental results are presented to illustrate the feasibility of the proposed method.