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Incremental training of support vector machines
journal contribution
posted on 2005-01-01, 00:00 authored by Alistair ShiltonAlistair Shilton, M Palaniswami, D Ralph, A C TsoiWe 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.
History
Journal
IEEE transactions on neural networksVolume
16Issue
1Pagination
114 - 131Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
1045-9227Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2005, IEEEUsage metrics
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No categories selectedKeywords
Active set methodIncremental trainingScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringquadratic programmingsupport vector machines (SVMs)warm start algorithm
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