Support Vector machines and related kernel methods

Dr Colin Campbell
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.
  • Computational Mathematics and Applications Seminar