Learning with Support Vector Machines
Colin Campbell (University of Bristol) and Yiming Ying (University of Exeter)
Published in the Series:
Synthesis Lectures on Artificial Intelligence and Machine Learning
Morgan & Claypool Publishers, San Rafael, USA (February 15, 2011)
Support Vectors Machines have become a well established tool within machine learning.
In this book we give a concise 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.