Abstract
In this paper we give a new, fast iterative algorithm for support
vector machine (SVM) classifier design. The problem is converted
to a problem of computing the nearest point between two convex polytopes.
The suitability of two classical nearest point algorithms, due to
Gilbert, and Mitchell, Dem'yanov and Malozemov, is studied. Ideas
from both these algorithms are combined and modified to derive our
fast algorithm. Comparitive computational evaluation of our algorithm
against powerful SVM methods such as Platt's Sequential Minimal
Optimization shows that our algorithm is very competitive.
Users
Please
log in to take part in the discussion (add own reviews or comments).