Full Text:   <2385>

CLC number: TP181

On-line Access: 2011-11-04

Received: 2011-01-05

Revision Accepted: 2011-08-16

Crosschecked: 2011-09-28

Cited: 3

Clicked: 3042

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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


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.

@article{title="Novel linear search for support vector machine parameter selection",
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",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%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

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

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


[1]Ahn, J., 2010. A stable hyperparameter selection for the Gaussian RBF kernel for discrimination. Statist. Anal. Data Min., 3(3):142-148.

[2]Bengio, Y., 2000. Gradient-based optimization of hyperparameters. Neur. Comput., 12(8):1889-1900.

[3]Bi, J., Bennett, K.P., 2003. A geometric approach to support vector regression. Neurocomputing, 55(1-2):79-108.

[4]Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S., 2002. Choosing multiple parameters for support vector machines. Mach. Learn., 46(1/3):131-159.

[5]Cherkassky, V., Ma, Y.Q., 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neur. Networks, 17(1):113-126.

[6]Cristianini, N., Kandola, J., Elisseeff, A., Shawe-Taylor, J., 2006. On kernel target alignment. Innov. Mach. Learn., 194:205-256.

[7]Deng, N.Y., Tian, Y.G., 2004. A New Method in Data Mining—Support Vector Machine. Science Press, Beijing, China (in Chinese).

[8]Duan, K., Keerthi, S.S., Poo, A.N., 2003. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing, 51:41-59.

[9]Gijsberts, A., Metta, G., Rothkrantz, L., 2010. Evolutionary optimization of least-squares support vector machines. Data Min., 8(4):277-297.

[10]Huang, C.L., Dun, J.F., 2008. A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Appl. Soft Comput., 8(4):1381-1391.

[11]Huang, C.L., Wang, C.J., 2006. A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl., 31(2):231-240.

[12]Keerthi, S.S., Lin, C.J., 2003. Asymptotic behaviors of support vector machines with Gaussian kernel. Neur. Comput., 15(7):1667-1689.

[13]Kim, D., Song, J., Lee, J., Choi, B., 2007. Support vector machine learning for region-based image retrieval with relevance feedback. ETRI J., 29(5):700-702.

[14]Lau, K.W., Wu, Q.H., 2008. Local prediction of non-linear time series using support vector regression. Pattern Recogn., 41(5):1539-1547.

[15]Po, L.M., Ma, W.C., 2002. A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circ. Syst. Video Technol., 6(3):313-317.

[16]Roohi, M., Mirjalily, G., Sadeghi, M.T., 2007. Face Detection Using a Modified SVM-Based Classifier. 7th Int. Conf. on Computational Intelligence and Multimedia Applications, p.356-360.

[17]Sapankevych, N., Sankar, R., 2009. Time series prediction using support vector machines, a survey. IEEE Computat. Intell. Mag., 4(2):24-38.

[18]Scholkopf, B., Smola, A.J., 2002. Learning with Kernels. MIT Press, Cambridge.

[19]Suykens, J.A.K., Vandewalle, J., de Moor, B., 2001. Optimal control by least squares support vector machines. Neur. Networks, 14(1):23-35.

[20]Vapnik, V., Golowich, S.E., Smola, A., 1997. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems 9 – Proc. Conf. on Advances in Neural Information Processing Systems, p.281-287.

[21]Vladimir, N., Vapnik, V., 2000. The Nature of Statistical Learning Theory. Springer Verlag, New York.

[22]Wang, T.Y., Chiang, H.M., 2007. Fuzzy support vector machine for multi-class text categorization. Inform. Process. Manag., 43(4):914-929.

[23]Zhu, S., Ma, K.K., 1997. A New Diamond Search Algorithm for Fast Block Matching Motion Estimation. Proc. Int. Conf. on Information, Communications and Signal Processing, 1:292-296.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE