Least squares support vector machine on morlet wavelet kernel function

被引：0

作者：

Wu, FF ^{[1
]}

Zhao, YL ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Inst Neocomp, Xian 710049, Peoples R China

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Based on the wavelet decomposition and conditions of the support vector kernel function, Morlet wavelet kernel function for support vector machine (SVM) is proposed, which is a kind of approximately orthonormal function. This kernel function can simulate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. According to the wavelet kernel function and the regularization theory, Least squares support vector machine on Morlet wavelet kernel function (LS-MWSVM) is proposed to simplify the process of MWSVM. The LS-MWSVM is then applied to the regression analysis or classifying. Experiment results show that the regression's precision is improved by LS-MWSVM compared with LS-SVM whose kernel function is Gauss function under the same conditions.

引用

页码：327 / 331

页数：5

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