Neural Computing and Applications

, Volume 23, Issue 1, pp 175–185

An ε-twin support vector machine for regression

Original Article

DOI: 10.1007/s00521-012-0924-3

Cite this article as:
Shao, YH., Zhang, CH., Yang, ZM. et al. Neural Comput & Applic (2013) 23: 175. doi:10.1007/s00521-012-0924-3


This study proposes a new regressor—ε-twin support vector regression (ε-TSVR) based on TSVR. ε-TSVR determines a pair of ε-insensitive proximal functions by solving two related SVM-type problems. Different form only empirical risk minimization is implemented in TSVR, the structural risk minimization principle is implemented by introducing the regularization term in primal problems of our ε-TSVR, yielding the dual problems to be stable positive definite quadratic programming problems, so can improve the performance of regression. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results for both artificial and real datasets show that, compared with the popular ε-SVR, LS-SVR and TSVR, our ε-TSVR has remarkable improvement of generalization performance with short training time.


Machine learning Support vector machines Regression Twin support vector machine Successive overrelaxation 

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Zhijiang College, Zhejiang University of TechnologyHangzhouPeople’s Republic of China
  2. 2.Department of Mathematics, Information School, Renmin University of ChinaBeijingPeople’s Republic of China
  3. 3.College of Science China Agricultural UniversityBeijingPeople’s Republic of China

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