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Neural Computing and Applications

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01-07-2013 | Original Article

An ε-twin support vector machine for regression

Authors: Yuan-Hai Shao, Chun-Hua Zhang, Zhi-Min Yang, Ling Jing, Nai-Yang Deng

Published in: Neural Computing and Applications | Issue 1/2013

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Abstract

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.

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Title: An ε-twin support vector machine for regression
Authors: Yuan-Hai Shao
Chun-Hua Zhang
Zhi-Min Yang
Ling Jing
Nai-Yang Deng
Publication date: 01-07-2013
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0924-3

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