Hamel L.H. Knowledge Discovery with Support Vector Machines

New Jersey: Wiley, 2009. - 267 p.
This book has been written to provide an introduction to this important class of machine learning algorithms with a minimum of technical background in order to make this material as widely accessible as possible. With the exception of some basic notions in calculus and probability theory, the book is completely self-contained. Important concepts in linear algebra and optimization theory are carefully motivated and introduced. Specifically, we have excluded any technical material that does not contribute directly to the understanding of support vector machines. Many other excellent textbooks are available today that develop support vector machines in much more technical detail than is provided here. These books should be accessible to the reader after reading this book. It is worth mentioning that we develop support vector machines from a computational perspective rather than from the traditional statistical perspective.
The book is aimed at upper-level undergraduate as well as beginning graduate students who want to learn more about support vector machines or who are pursuing research in machine learning and related areas. It should also prove a gentle tutorial on support vector machines for machine learning researchers and data analysts. The main objective of this book is to provide the necessary background to work with existing machine learning tool sets that include support vector machines as part of their suite of components. Once the material in this book has been mastered, the reader will be able to apply standard support vector machine learning algorithms to his or her problems with concrete insights as to what is going on under the hood.