中文

Operations Research Transactions ›› 2018, Vol. 22 ›› Issue (2): 55-65.doi: 10.15960/j.cnki.issn.1007-6093.2018.02.005

Special Issue: 庆贺邓乃扬教授80华诞专辑

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From support vector machine to nonparallel support vector machine

SHAO Yuanhai1,*  YANG Kaili2  LIU Mingzeng3   WANG Zhen4   LI ChunNa5  CHEN WeiJie5   

  1. 1.  School of Economics and Management, Hainan University, Haikou 570228, China; 2. College of Science, Zhejiang University of Technology, Hangzhou  310023, China; 3. School of Science, Dalian University of Technology, Panjin 124221, Liaoning, China; 4. School of Mathematical Sciences, Inner Mongolia University, Hohhot 010021, China; 5. Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
  • Received:2017-09-30 Online:2018-06-15 Published:2018-06-15

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Abstract:

Nonparallel support vector machine (NSVM) is the extension of support vector machine (SVM), and it has been widely studied in recent years. The NSVM constructs nonparallel support hyperplanes for each class, which can describe the distribution of different classes, thus applicable to wider problems. However, the study of the relationship between NSVM and SVM is rarely. And to now, there is no NSVM could be degenerate or equivalent to the standard SVM. We start from this view of point, and construct a new NSVM model. Our model not only can be reduced to the standard SVM, preserves the sparsity and kernel scalability, but also can describe the distribution of the different classes. At last, we compare our model with start-of-art SVMs and NSVMs on benchmark datasets, and confirm the superiority of proposed NSVM.

Key words: data mining, support vector machines, loss function, kernel learning, nonparallel support vector machines