On approximate solutions to support vector machines

Dongwei Cao, Daniel Boley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to extract the representatives. As practical variations, we also propose two efficient implementations of the proposed algorithm, where approximations to kernel k-means are used. The proposed algorithms are compared against the standard training algorithm over real data sets.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Pages534-538
Number of pages5
ISBN (Print)089871611X, 9780898716115
DOIs
StatePublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: Apr 20 2006Apr 22 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining
Volume2006

Other

OtherSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD
Period4/20/064/22/06

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