The Research of Least Square Support Vector Machine Model and its Simplified Method

Wen Jie Zhao; Tao Zhang

doi:10.4028/www.scientific.net/AMM.241-244.1719

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 241-244The Research of Least Square Support Vector...

The Research of Least Square Support Vector Machine Model and its Simplified Method

Abstract:

A simplified structure of the least square support vector machine (LS-SVM) model is proposed in this paper. Under the premise that the accuracy of LS-SVM model is unchanged, a small amount of training samples are chosen, which further fit this model by LS-SVM modeling. Finally, a typical nonlinear problem is taken as example to test the performance of this simplified model and the simulation results show that this simplified method proposed in this paper is effective.

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Periodical:

Applied Mechanics and Materials (Volumes 241-244)

Pages:

1719-1723

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.241-244.1719

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Online since:

December 2012

Authors:

Wen Jie Zhao, Tao Zhang

Keywords:

Complexity of Model, Least Squares Support Vector Machines (Ls-SVM), Modeling, Simplify

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References

[1] Vapnik V N. The nature of statistical learning theory [M]. New York: Springer, 2000.

Google Scholar

[2] Deng Naiyang and Tian Yingjie, Support vector machine—theory, algorithm and development [M], Beijing: Sience Press, (2009)

Google Scholar

[3] Rui Zhangxian and Xiao Haibo, Support vector machine and its application [J], Fujian Computer, 2007(4).-110-110,192

Google Scholar

[4] Wan Hui, Wei Yan. An improved least square support vector machines algorithm [J]. Journal of Chongqing Normal University(Natural Science), 2009, 13(4):626-630.

Google Scholar

[5] Zhou Xin-ran, Teng Zhao-sheng. An online sparse LSSVM and its application in system modeling [J]. Journal of Human University(Natural Science), 2010,37(4):37-41.

Google Scholar

[6] Wu Zong-liang, Dou Heng. New sparse least squares support vector machine

Google Scholar

[7] Wang Ding-cheng, Jiang Bin. Online sparse least square support vector machines regression [J]. Control and decision, 2007,22(2):132-137.

Google Scholar

[8] Gan Liang-zhi, Sun Zong-hai, Sun You-xian. Sparse least square support vector machines [J]. Journal of Zhejiang University(Engineering Science), 2007,41(2):245-248.

Google Scholar

[9] Yang Mei, Liu Yu and Kong Bo, Research on Parameters Selection Method of SVR Model [J], Computer Era, 2009(11).-53-55.

Google Scholar

[10] De Brabanter K, Karsmakers P, Ojeda F. LS-SVMlab toolbox user's guide[R]. ESAT-SISTA Technical Report, 2010.

Google Scholar

[11] Dong Ting, SVM Algorithm Realized in MATLAB [J], Journal of Yulin College, 2008,18(4).-94-96.

Google Scholar

[12] Gunn S. Support Vector Machines for Classification and Regression[R].Technical Report for University of Southampton, 1998.

Google Scholar