Learning with Support Vector Machines

by Colin Campbell, Yiming Ying

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

95 pages, ebook

First published September 9, 2010

About the author

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Colin Campbell has worked in ELT for 28 years as a teacher, trainer, Director of Studies and Consultant. During that time he worked in a number of different countries, including Spain, Italy, Poland, Ireland and Estonia. He has worked at the University of Reading since 1998 where he teaches English for Academic Purposes.