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Neural Computing and Applications

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01.05.2013 | Original Article

Multiple birth support vector machine for multi-class classification

verfasst von: Zhi-Xia Yang, Yuan-Hai Shao, Xiang-Sun Zhang

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

For multi-class classification problem, a novel algorithm, called as multiple birth support vector machine (MBSVM), is proposed, which can be considered as an extension of twin support vector machine. Our MBSVM has been compared with the several typical support vector machines. From theoretical point of view, it has been shown that its computational complexity is remarkably low, especially when the class number K is large. Based on our MBSVM, the dual problems of MBSVM are equivalent to symmetric mixed linear complementarity problems to which successive overrelaxation (SOR) can be directly applied. We establish our SOR algorithm for MBSVM. The SOR algorithm handles one data point at a time, so it can process large dataset that need no reside in memory. From practical point of view, its accuracy has been validated by the preliminary numerical experiments.

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Titel: Multiple birth support vector machine for multi-class classification
verfasst von: Zhi-Xia Yang
Yuan-Hai Shao
Xiang-Sun Zhang
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1108-x

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