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Support Vector Machines for Business Applications
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Author(s): Brian C. Lovell (NICTA & The University of Queensland, Australia)and Christian J. Walder (Max Planck Institute for Biological Cybernetics, Germany)
Copyright: 2006
Pages: 24
Source title:
Business Applications and Computational Intelligence
Source Author(s)/Editor(s): Kevin Voges (University of Canterbury, New Zealand)and Nigel Pope (Griffith University, Australia)
DOI: 10.4018/978-1-59140-702-7.ch014
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Abstract
This chapter discusses the use of Support Vector Machines (SVM) for business applications. It provides a brief historical background on inductive learning and pattern recognition, and then an intuitive motivation for SVM methods. The method is compared to other approaches, and the tools and background theory required to successfully apply SVM to business applications are introduced. The authors hope that the chapter will help practitioners to understand when the SVM should be the method of choice, as well as how to achieve good results in minimal time.
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