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Fuzzy Multi-Sphere Support Vector Data Description

  • Trung Le
  • Dat Tran
  • Wanli Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.

Keywords

Kernel Methods Fuzzy Interference Support Vector Data Description Multi-Sphere Support Vector Data Description 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Trung Le
    • 1
  • Dat Tran
    • 2
  • Wanli Ma
    • 2
  1. 1.HCM City University of EducationVietnam
  2. 2.University of CanberraAustralia

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