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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2014

01.10.2014 | Original Article

ν-Nonparallel support vector machine for pattern classification

verfasst von: Yingjie Tian, Qin Zhang, Dalian Liu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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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.
Vorheriger Artikel Modeling and simulation of static loads for wind power applications
Nächster Artikel Convergence and stability analysis of a novel iteration method for fractional biological population equation

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Metadaten
Titel
ν-Nonparallel support vector machine for pattern classification
verfasst von
Yingjie Tian
Qin Zhang
Dalian Liu
Publikationsdatum
01.10.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1575-3

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