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.
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Schebesch, K.B., Stecking, R. (2005). Extracting Rules from Support Vector Machines. In: Fleuren, H., den Hertog, D., Kort, P. (eds) Operations Research Proceedings 2004. Operations Research Proceedings, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27679-3_51
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DOI: https://doi.org/10.1007/3-540-27679-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24274-1
Online ISBN: 978-3-540-27679-1
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