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Improvements to Platt's SMO algorithm for SVM classifier design (2001)

Abstract
This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm 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. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.

Publication details
Download http://eprints.iisc.ernet.in/17038/1/fulltext.pdf
Publisher MIT Press
Repository ePrints@iisc (India)
Keywords Computer Science & Automation
Type Journal Article, PeerReviewed
Relation http://www.mitpressjournals.org/doi/abs/10.1162/089976601300014493
http://eprints.iisc.ernet.in/17038/