NDLI: ν-Nonparallel support vector machine for pattern classification

Content Provider	SpringerLink
Author	Tian, Yingjie Zhang, Qin Liu, Dalian
Copyright Year	2014
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.
Starting Page	1007
Ending Page	1020
Page Count	14
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	25
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-04-12
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Support vector machine Twin support vector machines Nonparallel Structural risk minimization principle Sparseness Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

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