Least-Squares Wavelet Kernel Method for Regression Estimation

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 ke...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 582 - 591
Main Authors Wen, Xiangjun, Xu, Xiaoming, Cai, Yunze
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Kernel Operator
Little Square Support Vector Machine
Reproduce Kernel Hilbert Space
Support Vector Machine
Wavelet Frame
Kernel function
Statistical analysis
Wavelet transformation
Least squares method
Vector support machine
Hilbert space
Feasibility
Regression analysis
Kernel method
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Summary: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.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_74