Newton Algorithm of Twin Support Vector Regression

doi:10.3969/j.issn.1000-3428.2013.01.041

Computer Engineering ›› 2013, Vol. 39 ›› Issue (1): 191-194.

• Networks and Communications • Previous Articles Next Articles

Newton Algorithm of Twin Support Vector Regression

ZHENG Feng-de, ZHANG Hong-bin

(College of Computer Science, Beijing University of Technology, Beijing 100124, China)

Received:2011-10-14 Revised:2011-12-20 Online:2013-01-15 Published:2013-01-13

双支持向量回归的牛顿算法

郑逢德，张鸿宾

(北京工业大学计算机学院，北京 100124)

作者简介:郑逢德(1980－)，男，博士研究生，主研方向：核方法，机器学习；张鸿宾，教授、博士生导师
基金资助:
国家自然科学基金资助项目(60775011)

Abstract

Abstract: For improving the learning speed of Support Vector Regression(SVR), this paper proposes a Newton algorithm for Twin Support Vector Regression(TSVR) that tries to find a pair of nonparallel planes by solving two related SVR-type problems and converts the classical Quadratic Programming Problem(QPP) to two small unconstrained optimization problems. Each of the unconstrained optimization problems is solved by Newton algorithm. Experimental results show that the proposed algorithm has good fitting ability as SVR and TSVR, and can reduce the training time.

Key words: machine learning, pattern recognition, Support Vector Regression(SVR), Twin Support Vector Regression(TSVR), unconstrained optimization, Newton algorithm

摘要： 为提高支持向量回归的运算速度，提出一种双支持向量回归的牛顿算法。求解2个只带一组约束的支持向量问题，以减少运算量，将2个约束优化问题转化为无约束最优化问题，并采用牛顿迭代算法求解。实验结果表明，在保证与支持向量回归和双支持向量回归拟合能力相当的同时，该算法能减少训练时间。

关键词: 机器学习, 模式识别, 支持向量回归, 双支持向量回归, 无约束优化, 牛顿算法

CLC Number:

TP301.6

ZHENG Feng-De, ZHANG Hong-Bin. Newton Algorithm of Twin Support Vector Regression[J]. Computer Engineering, 2013, 39(1): 191-194.

郑逢德, 张鸿宾. 双支持向量回归的牛顿算法[J]. 计算机工程, 2013, 39(1): 191-194.

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https://www.ecice06.com/EN/Y2013/V39/I1/191

References

[1] Cristianini N, Taylor J S. Kernel Methods for Pattern Analysis[M]. Cambridge, UK: Cambridge University Press, 2004.
[2] Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition[J]. Data Mining Knowledge Discovery, 1998, 2(2): 121-167.
[3] Schokopf B, Smola A J. Learning with Kernels[M].
Cambridge, USA: MIT Press, 2002.
[4] Vapnik V. The Nature of Statistical Learning Theory[M]. New York, USA: Springer, 1995.
[5] Vapnik V. An Overview of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999.
[6] Cortes C, Vapnik V. Support Vector Networks[J]. Machine Learning, 1995, 20(3): 273-297.
[7] Cristianini N, Taylor J S. An Introduction to Support Vector Machines[M]. Cambridge, UK: Cambridge University Press, 2000.
[8] 周金柱, 黄进. 集成先验知识的多核线性规划支持向量回归[J]. 自动化学报, 2011, 37(3): 360-370.
[9] Peng Xinjun. TSVR: An Efficient Twin Support Vector Machine for Regression[J]. Neural Networks, 2010, 23(3): 342-365.
[10] Khemchandani R, Chandra S. Twin Support Vector Machines for Pattern Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910.
[11] Khemchandani R, Chandra S. On Finite Newton Method for Support Vector Regression[J]. Neural Computing and Applications, 2010, 19(7): 967-977.
[12] Mangasarian O L, Musicant D R. Lagrangian Support Vector Machine[J]. Journal of Machine Learning Research, 2001, 1: 161-177.
[13] Balasundaram S K. On Lagrangian Support Vector Regression[J]. Expert Systems with Applications, 2010, 37(12): 8784-8793.
[14] Mangasarian O L, Musicant D R. Finite Newton Method for Lagrangian Support Vector Machine[J]. Neuro- computing, 2003, 55(1-2): 39-55.
[15] Gunn S R. SVM Matlab Toolbox[EB/OL]. (2010-11-21). http://www.isis.ecs.soton.ac.uk/resources/svminfo.
[16] StatLib. Datasets Archive[EB/OL]. (2010-10-21). http://lib. stat.cmu.edu/datasets/.
[17] LIACC. Regression DataSets[EB/OL]. (2010-08-21). http:// www.liaad.up.pt/~ltorgo/Regression/DataSets.html.
[18] Blake C I, Merz C J. UCI Repository for Machine Learning Databases[EB/OL]. (2009-08-21). http://www.ics.uci. edu/_mlearn/MLRepository.html.

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双支持向量回归的牛顿算法

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