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  Support Vector Machines for Business Applications

Lovell, B., & Walder, C. (2008). Support Vector Machines for Business Applications. In G. Felici, & C. Vercellis (Eds.), Mathematical methods for knowledge discovery and data mining (pp. 82-100). Hershey, PA, USA: Information Science Reference.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-1DCC-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1DCD-7
Genre: Book Chapter

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 Creators:
Lovell, BC, Author
Walder, C1, 2, 3, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_2528702              

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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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 Dates: 2008
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.4018/978-1-59904-528-3.ch005
 Degree: -

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Title: Mathematical methods for knowledge discovery and data mining
Source Genre: Book
 Creator(s):
Felici, G, Editor
Vercellis, C, Editor
Affiliations:
-
Publ. Info: Hershey, PA, USA : Information Science Reference
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 82 - 100 Identifier: ISBN: 978-1-59904-528-3