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Fifth IEEE International Conference on Data Mining (ICDM'05)
Training Support Vector Machines Using Gilbert?s Algorithm
Houston, Texas
November 27-November 30
ISBN: 0-7695-2278-5
Shawn Martin, Sandia National Laboratories
Support Vector Machines are classifiers designed around the computation of an optimal separating hyperplane. This hyperplane is typically obtained by solving a constrained quadratic programming problem, but may also be located by solving a nearest point problem. Gilbert?s Algorithm can be used to solve this nearest point problem but is unreasonably slow. In this paper we present a modified version of Gilbert?s Algorithm for the fast computation of the Support Vector Machine hyperplane. We then compare our algorithm with the Nearest Point Algorithm and with Sequential Minimal Optimization.
Index Terms:
Support Vector Machines, Gilbert?s Algorithm, Nearest Point Algorithm, Sequential Minimal Optimization
Citation:
Shawn Martin, "Training Support Vector Machines Using Gilbert?s Algorithm," icdm, pp.306-313, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
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