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Support Vector Machines and Fuzzy Systems

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Soft Computing for Knowledge Discovery and Data Mining

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Fuzzy set theory and fuzzy logic provide tools for handling uncertainties in data mining tasks. To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for data mining and pattern recognition problems, support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high (or even infinite) dimensional feature space. This chapter presents a survey of the connection between fuzzy classifiers and kernel machines. A significant portion of the chapter is built upon material from articles we have written, in particular (Chen and Wang, 2003a, Chen and Wang, 2003b).

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Author information

Authors and Affiliations

  1. Dept. of Computer and Information Science, The University of Mississippi, MS 38655, USA

    Yixin Chen

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  1. Yixin Chen
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Editors and Affiliations

  1. Tel Aviv University, 69978, Tel Aviv, Israel

    Oded Maimon

  2. Ben-Gurion University, 84105, Beer-Sheva, Israel

    Lior Rokach

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Chen, Y. (2008). Support Vector Machines and Fuzzy Systems. In: Maimon, O., Rokach, L. (eds) Soft Computing for Knowledge Discovery and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-69935-6_9

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  • DOI: https://doi.org/10.1007/978-0-387-69935-6_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-69934-9

  • Online ISBN: 978-0-387-69935-6

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