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pubmed-article:15732393pubmed:dateCreated2005-2-28lld:pubmed
pubmed-article:15732393pubmed:abstractTextWe propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.lld:pubmed
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pubmed-article:15732393pubmed:authorpubmed-author:PalaniswamiMMlld:pubmed
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pubmed-article:15732393pubmed:volume16lld:pubmed
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pubmed-article:15732393pubmed:pagination114-31lld:pubmed
pubmed-article:15732393pubmed:dateRevised2006-11-15lld:pubmed
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pubmed-article:15732393pubmed:year2005lld:pubmed
pubmed-article:15732393pubmed:articleTitleIncremental training of support vector machines.lld:pubmed
pubmed-article:15732393pubmed:affiliationCenter of Expertise on Networked Decision and Sensor Systems, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia. apsh@ee.mu.oz.aulld:pubmed
pubmed-article:15732393pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:15732393pubmed:publicationTypeComparative Studylld:pubmed
pubmed-article:15732393pubmed:publicationTypeEvaluation Studieslld:pubmed
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