A Novel Least Squares Support Vector Machine Kernel for Approximation

被引:0
|
作者
Mu, Xiangyang [1 ]
Gao, Weixin [1 ]
Tang, Nan [1 ]
Zhou, Yatong [2 ]
机构
[1] Xian Shiyou Univ, Sch Elect Engn, Xian 710065, Peoples R China
[2] Hebei Univ Technol, Sch Informat Engn, Tianjin 300401, Peoples R China
关键词
Least squares support vector machine; Scaling kernel; Reproducing kernel Hilbert spaces; Approximation;
D O I
10.1109/WCICA.2008.4593650
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Support Vector Machine(SVM) is receiving considerable attention for its Superior ability to solve nonlinear classification, function estimation and density estimation. Least Squares Support Vector Machines (LS-SVM) are re-formulations to the standard SVMs. Motivated by the theory of multi-scale representations of signals and wavelet transforms, this paper presents a way for building a wavelet-based reproducing kernel Hilbert spaces (RKHS) and its associate scaling kernel for least squares support vector machines (LS-SVM). The RKHS built is a multiresolution scale subspace, and the scaling kernel is constructed by using a scaling function with its different dilations and translations. Compared to the traditional kernels, approximation results illustrate that the LS-SVM with scaling kernel enjoys two advantages: (1) it can approximate arbitrary signal and owns better approximation performance; (2) it can implement multi-scale approximation.
引用
收藏
页码:4510 / +
页数:2
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