Learning with Support Vector Machines

ICG Campbell, Ying Yiming

Research output: Book/ReportAuthored book

87 Citations (Scopus)

Abstract

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.
Translated title of the contributionLearning with Support Vector Machines
Original languageEnglish
PublisherMorgan and Claypool
Number of pages100
ISBN (Print)9781608456161
DOIs
Publication statusPublished - 2011

Bibliographical note

Other: with Yiming Ying

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