Canonical duality solution for alternating support vector machine

Yubo Yuan

doi:10.3934/jimo.2012.8.611

Article Contents

2012, Volume 8, Issue 3: 611-621. Doi: 10.3934/jimo.2012.8.611

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Canonical duality solution for alternating support vector machine

Yubo Yuan^1,

1.
Department of Computer Science and Technology, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237

Received: June 2011

Revised: December 2011

Published: July 2012

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Abstract

Support vector machine (SVM) is one of the most popular machine learning methods and is educed from a binary data classification problem. In this paper, the canonical duality theory is used to solve the normal model of SVM. Several examples are illustrated to show that the exact solution can be obtained after the canonical duality problem being solved. Moreover, the support vectors can be located by non-zero elements of the canonical dual solution.

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Mathematics Subject Classification: Primary: 68T10; Secondary:90C27.

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