A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design

Keerthi, SS and Shevade, SK and Bhattacharyya, C and Murthy, KRK (2000) A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design. In: IEEE Transactions on Neural Networks, 11 (1). pp. 124-136.

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Abstract

In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell ct at, is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm, For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and FrieB is used to convert it to a problem in which there are no classification violations, Comparative computational evaluation of our algorithm against powerful SVM methods such as Platts sequential minimal optimization shows that our algorithm is very competitive.

Item Type:	Journal Article
Publication:	IEEE Transactions on Neural Networks
Publisher:	IEEE
Additional Information:	©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Classification;nearest point algorithm;quadratic programming;support vector machine
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	25 Aug 2008
Last Modified:	19 Sep 2010 04:15
URI:	http://eprints.iisc.ac.in/id/eprint/1740

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