Training twin support vector regression via linear programming

Authors:
Ping Zhong;Yitian Xu;Yaohong Zhao
Affiliations:
China Agricultural University, College of Science, 100083, Beijing, China;China Agricultural University, College of Science, 100083, Beijing, China;China Agricultural University, College of Science, 100083, Beijing, China
Venue:
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
Year:
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.