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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2012

01-03-2012 | Original Article

Training twin support vector regression via linear programming

Authors: Ping Zhong, Yitian Xu, Yaohong Zhao

Published in: Neural Computing and Applications | Issue 2/2012

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Abstract

This paper improves the recently proposed twin support vector regression (TSVR) by formulating it as a pair of linear programming problems instead of quadratic programming problems. The use of 1-norm distance in the linear programming TSVR as opposed to the square of the 2-norm in the quadratic programming TSVR leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on artificial and benchmark datasets.
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Appendix
Available only for authorised users
Footnotes
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Metadata
Title
Training twin support vector regression via linear programming
Authors
Ping Zhong
Yitian Xu
Yaohong Zhao
Publication date
01-03-2012
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 2/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0525-6

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