In the last few years there have been very significant developments in the theoretical understanding of Support Vector Machines （SVMs） as well as algorithmic strategies for implementing them， and applications of the approach to practical problems. We believe that the topic has reached the point at which it should perhaps be viewed as its own subfield of machine learning， a subfield which promises much in both theoretical insights and practical usefulness. Despite reaching this stage of development， we were aware that no organic integrated introduction to the subject had yet been attempted. Presenting a comprehensive introduction to SVMs requires the synthesis of a surprisingly wide range of material， including dual representations， feature spaces， learning theory， optimisation theory， and algorithmics. Though active research is still being pursued in all of these areas， there are stable foundations in each that together form the basis for the SVM concept. By building from those stable foundations， this book attempts a measured and accessible introduction to the subject of Support Vector Machines. The book is intended for machine learning students and practitioners who want a gentle but rigorous introduction to this new class of learning systems. It is organised as a textbook that can be used either as a central text for a course on SVMs， or as an additional text in a neural networks， machine learning， or pattern recognition class. Despite its organisation as a textbook， we have kept the presentation self-contained to ensure that it is suitable for the interested scientific reader not necessarily working directly in machine learning or computer science. In this way the book should give readers from other scientific disciplines a practical introduction to Support Vector Machines enabling them to apply the approach to problems from their own domain. We have attempted to provide the reader with a route map through the rigorous derivation of the material. For this reason we have only included proofs or proof sketches where they are accessible and where we feel that they enhance the understanding of the main ideas. Readers who are interested in the detailed proofs of the quoted results are referred to the original articles.
