Support Vector Machines are a new and very promising approach to
machine learning. They can be applied to a wide range of tasks such as
classification, regression, novelty detection, density estimation,
etc. The approach is motivated by statistical learning theory and the
algorithms have performed well in practice on important applications
such as handwritten character recognition (where they currently give
state-of-the-art performance), bioinformatics and machine vision. The
learning task typically involves optimisation theory (linear, quadratic
and general nonlinear programming, depending on the algorithm used).
In fact, the approach has stimulated new questions in optimisation
theory, principally concerned with the issue of how to handle problems
with a large numbers of variables. In the first part of the talk I will
overview this subject, in the second part I will describe some of the
speaker's contributions to this subject (principally, novelty
detection, query learning and new algorithms) and in the third part I
will outline future directions and new questions stimulated by this
research.