DME Handout: Support Vector Machines School of Informatics, University of

Abstract

Support Vector Machines (SVMs) are a relatively new concept in supervised learning, but since the publication of [3] in 1995 they have been applied to a wide variety of problems. In many ways the application of SVMs to almost any learning problem mirrors the enthusiasm (and fashionability) that was observed for neural networks in the second half of the 1980’s. The ingredients of the SVM had, in fact, been around for a decade or so, but they were not put together until the early 90’s. The two key ideas of support vector machines are (i) The maximum margin solution for a linear classifier. (ii) The “kernel trick”; a method of expanding up from a linear classifier to a non-linear one in an efficient manner. Below we discuss these key ideas in turn, and then go on to consider support vector regression and some example applications of SVMs. Further reading on the topic can be found in [2], [7] and [4]. For those keen to keep up with the latest results, the web sit