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CLC number: TP181

On-line Access: 2011-11-04

Received: 2011-01-05

Revision Accepted: 2011-08-16

Crosschecked: 2011-09-28

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.11 P.885-896

http://doi.org/10.1631/jzus.C1100006


Novel linear search for support vector machine parameter selection


Author(s):  Hong-xia Pang, Wen-de Dong, Zhi-hai Xu, Hua-jun Feng, Qi Li, Yue-ting Chen

Affiliation(s):  State Key Laboratory of Optical Instrumentation, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   malinda2568@sina.com, fenghj@zju.edu.cn

Key Words:  Support vector machine (SVM), Rough line rule, Parameter selection, Linear search, Motion prediction


Hong-xia Pang, Wen-de Dong, Zhi-hai Xu, Hua-jun Feng, Qi Li, Yue-ting Chen. Novel linear search for support vector machine parameter selection[J]. Journal of Zhejiang University Science C, 2011, 12(11): 885-896.

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author="Hong-xia Pang, Wen-de Dong, Zhi-hai Xu, Hua-jun Feng, Qi Li, Yue-ting Chen",
journal="Journal of Zhejiang University Science C",
volume="12",
number="11",
pages="885-896",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100006"
}

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%T Novel linear search for support vector machine parameter selection
%A Hong-xia Pang
%A Wen-de Dong
%A Zhi-hai Xu
%A Hua-jun Feng
%A Qi Li
%A Yue-ting Chen
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 11
%P 885-896
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100006

TY - JOUR
T1 - Novel linear search for support vector machine parameter selection
A1 - Hong-xia Pang
A1 - Wen-de Dong
A1 - Zhi-hai Xu
A1 - Hua-jun Feng
A1 - Qi Li
A1 - Yue-ting Chen
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 11
SP - 885
EP - 896
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100006


Abstract: 
Selecting the optimal parameters for support vector machine (SVM) has long been a hot research topic. Aiming for support vector classification/regression (SVC/SVR) with the radial basis function (RBF) kernel, we summarize the rough line rule of the penalty parameter and kernel width, and propose a novel linear search method to obtain these two optimal parameters. We use a direct-setting method with thresholds to set the epsilon parameter of SVR. The proposed method directly locates the right search field, which greatly saves computing time and achieves a stable, high accuracy. The method is more competitive for both SVC and SVR. It is easy to use and feasible for a new data set without any adjustments, since it requires no parameters to set.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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