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CLC number: TP18

On-line Access: 2013-09-05

Received: 2013-01-27

Revision Accepted: 2013-06-17

Crosschecked: 2013-08-07

Cited: 7

Clicked: 7247

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.9 P.722-732


Primal least squares twin support vector regression

Author(s):  Hua-juan Huang, Shi-fei Ding, Zhong-zhi Shi

Affiliation(s):  School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; more

Corresponding email(s):   dingsf@cumt.edu.cn

Key Words:  Twin support vector regression, Least squares method, Primal space, Stock prediction

Hua-juan Huang, Shi-fei Ding, Zhong-zhi Shi. Primal least squares twin support vector regression[J]. Journal of Zhejiang University Science C, 2013, 14(9): 722-732.

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%T Primal least squares twin support vector regression
%A Hua-juan Huang
%A Shi-fei Ding
%A Zhong-zhi Shi
%J Journal of Zhejiang University SCIENCE C
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%N 9
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%@ 1869-1951
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.CIIP1301

T1 - Primal least squares twin support vector regression
A1 - Hua-juan Huang
A1 - Shi-fei Ding
A1 - Zhong-zhi Shi
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 9
SP - 722
EP - 732
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.CIIP1301

The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space. However, this solution is affected by time and memory constraints when dealing with large datasets. In this paper, we present a least squares version for TSVR in the primal space, termed primal least squares TSVR (PLSTSVR). By introducing the least squares method, the inequality constraints of TSVR are transformed into equality constraints. Furthermore, we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space; thus, we need only to solve two systems of linear equations instead of two QPPs. Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational time. We further investigate its validity in predicting the opening price of stock.

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


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