Research on Nonlinear Modeling Method of Support Vector Machine with Wavelet Derivation Kernel Function

Ping An Wang; Xu Sheng Gan; Wen Ming Gao

doi:10.4028/www.scientific.net/AMM.687-691.1408

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Research on Nonlinear Modeling Method of Support...

Research on Nonlinear Modeling Method of Support Vector Machine with Wavelet Derivation Kernel Function

Abstract:

The model capability of Support Vector Machine (SVM) relies on the selection of kernel function. To obtain a better application modeling of SVM, the wavelet kernel function that satisfies Merce condition is introduced to use the kernel function of SVM, achieving a good effect. In the paper, on the basis of wavelet kernel function, a wavelet derivation kernel function is proposed in the application of SVM for higher accuracy. An actual example on nonlinear function approximation shows that SVM regression model has a satisfactory approximation effect, and also support an effective nonlinear modeling method.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1408-1411

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1408

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Online since:

November 2014

Authors:

Ping An Wang*, Xu Sheng Gan, Wen Ming Gao

Keywords:

Function Approximation, Kernel Function, Merce Condition, Support Vector Machine (SVM), Wavelet Analysis

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