Training Support Vector Machines Using Gilbert’s Algorithm
Shawn Martin · 2006
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.