Training Support Vector Machines Using Gilbert's Algorithm

Authors:
Shawn Martin
Affiliations:
Sandia National Laboratories
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

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Abstract

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.