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Improvements to the SMO algorithm for SVM regression

Shevade, SK and Keerthi, SS and Bhattacharyya, C and Murthy, KRK (2000) Improvements to the SMO algorithm for SVM regression. In: IEEE Transactions on Neural Networks, 11 (5). pp. 1188-1193.

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Abstract

This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal optimization (SMO) algorithm for support vector machine (SVM)regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression, These modified algorithms perform significantly faster than the original SMO on the datasets tried.

Item Type: Journal Article
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: Quadratic programming;regression;sequential minimal optimization (SMO) algorithm;support vector machine (SVM).
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 25 Aug 2008
Last Modified: 19 Sep 2010 04:15
URI: http://eprints.iisc.ernet.in/id/eprint/1687

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