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Support Vector Machines for Business Applications

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Walder,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0003-1DCC-8
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.