A unified approach to support vector machines

Shilton, Alistair and Palaniswami, Marimuthu 2008, A unified approach to support vector machines. In Verma, Brijesh and Blumenstein, Michael (ed), Pattern Recognition Technologies and Applications: Recent Advances, IGI Global, Hershey, Pa., pp.299-324, doi: 10.4018/978-1-59904-807-9.ch014.

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Title A unified approach to support vector machines
Author(s) Shilton, AlistairORCID iD for Shilton, Alistair orcid.org/0000-0002-0849-3271
Palaniswami, Marimuthu
Title of book Pattern Recognition Technologies and Applications: Recent Advances
Editor(s) Verma, Brijesh
Blumenstein, Michael
Publication date 2008
Chapter number 14
Total chapters 17
Start page 299
End page 324
Total pages 26
Publisher IGI Global
Place of Publication Hershey, Pa.
Summary This chapter presents a unified introduction to support vector machine (SVM) methods for binary classification, one-class classification, and regression. The SVM method for binary classification (binary SVC) is introduced first, and then extended to encompass one-class classification (clustering). Next, using the regularized risk approach as a motivation, the SVM method for regression (SVR) is described. These methods are then combined to obtain a single unified SVM formulation that encompasses binary classification, one-class classification, and regression (as well as some extensions of these), and the dual formulation of this unified model is derived. A mechanical analogy for binary and one-class SVCs is given to give an intuitive explanation of the operation of these two formulations. Finally, the unified SVM is extended to implement general cost functions, and an application of SVM classifiers to the problem of spam e-mail detection is considered.
ISBN 1599048078
Language eng
DOI 10.4018/978-1-59904-807-9.ch014
Indigenous content off
HERDC Research category BN.1 Other book chapter, or book chapter not attributed to Deakin
Copyright notice ©2008, IGI Global
Persistent URL http://hdl.handle.net/10536/DRO/DU:30125477

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