Extracting Rules from Support Vector Machines

Klaus B. Schebesch and Ralf Stecking
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Klaus B. Schebesch: Universität Bremen
Ralf Stecking: Universität Bremen

A chapter in Operations Research Proceedings 2004, 2005, pp 408-415 from Springer

Abstract: Abstract Support Vector Machines (SVM) from statistical learning are very powerful methods which can be used as (e.g.binary) classifiers or discriminators in a wide range of applications. Advantages of SVM are that weak prior assumptions about both model and data suffice. Moreover, optimization of the SVM essentially regularizes the emerging data model by restricting the model to special data points, the support vectors, usually a small subset from the training data. In our paper we discuss ways of detecting informative and typical subsets from SVM solutions, with the aim of extracting simple rules.

Keywords: Support Vector Machine; Support Vector; Linear Discriminant Analysis; Tenfold Cross Validation; Linear Support Vector Machine (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-540-27679-1_51

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DOI: 10.1007/3-540-27679-3_51

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