Least Squares Support Vector Machine on Moret Wavelet kernel Function

Fangfang Wu Fangfang Wu,Yinliang Zhao Yinliang Zhao

doi:10.1109/icnnb.2005.1614625

Fangfang Wu Fangfang Wu, Yinliang Zhao Yinliang Zhao

https://doi.org/10.1109/icnnb.2005.1614625

Copy DOI

Export

Save

Cite

Publication Date: Oct 13, 2005

Affiliation: Xi'an Jiaotong University

Abstract
Full-Text
Similar Papers

Abstract

Listen

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 this kind of function has already existed, and it is the precision is improved by LS-MWSVM, compared with LS-SVM whose kernel function is Gauss function under the same conditions

Full Text