My first exposure to Support Vector Machines came this spring when heard Sue Dumais
present impressive results on text categorization using this analysis technique. This issue's
collection of essays should help familiarize our readers with this interesting new racehorse
in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out
that a particular advantage of SVMs over other learning algorithms is that it can be analyzed
theoretically using concepts from computational learning theory, and at the same time can …

This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of a
very large quadratic programming (QP) optimization problem. SMO breaks this large QP
problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as an
inner loop. The amount of memory required for SMO is linear in the training set size, which …