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Neural Computing and Applications

, Volume 25, Issue 5, pp 1007–1020 | Cite as

ν-Nonparallel support vector machine for pattern classification

  • Yingjie Tian
  • Qin Zhang
  • Dalian Liu
Original Article

Abstract

In this paper, we propose a novel nonparallel hyperplane classifier, named ν-nonparallel support vector machine (ν-NPSVM), for binary classification. Based on our recently proposed method, i.e., nonparallel support vector machine (NPSVM), which has been proved superior to the twin support vector machines, ν-NPSVM is parameterized by the quantity ν to let ones effectively control the number of support vectors. By combining the ν-support vector classification and the ν-support vector regression together to construct the primal problems, ν-NPSVM inherits the advantages of ν-support vector machine so that enables us to eliminate one of the other free parameters of the NPSVM: the accuracy parameter ε and the regularization constant C. We describe the algorithm, give some theoretical results concerning the meaning and the choice of ν, and also report the experimental results on lots of data sets to show the effectiveness of our method.

Keywords

Support vector machine Twin support vector machines  Nonparallel Structural risk minimization principle Sparseness 

Notes

Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 11271361, 71331005), the CAS/SAFEA International Partnership Program for Creative Research Teams, Major International (Regional) Joint Research Project (No. 71110107026), and the Ministry of water resources’ special funds for scientific research on public causes (No. 201301094).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  2. 2.Department of Basic Course TeachingBeijing Union UniversityBeijingChina

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