This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
In supervised learning, the learning machine is given a training set of examples (
or inputs) with associated labels (or output values). Usually the examples are in
the form of attribute vectors, so that the input space is a subset of W. Once the
attribute vectors are available, a number of sets of hypotheses could be chosen
for the problem. Among these, linear functions are the best understood and
simplest to apply. Traditional statistics and the classical neural networks literature
have ...
