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Factorizing Class Characteristics via Group MEBs Construction

  • Ye Chen
  • Shaoning Pang
  • Nikola Kasabov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6444)

Abstract

Classic MEB (minimum enclosing ball) models characteristics of each class for classification by extracting core vectors through a (1 + ε)-approximation problem solving. In this paper, we develop a new MEB system learning the core vectors set in a group manner, called group MEB (g-MEB). The g-MEB factorizes class characteristic in 3 aspects such as, reducing the sparseness in MEB by decomposing data space based on data distribution density, discriminating core vectors on class interaction hyperplanes, and enabling outliers detection to decrease noise affection. Experimental results show that the factorized core set from g-MEB delivers often apparently higher classification accuracies than the classic MEB.

Keywords

Minimum Enclosing Ball Core Vector Machine Group Minimum Enclosing Ball 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ye Chen
    • 1
    • 2
  • Shaoning Pang
    • 1
    • 2
  • Nikola Kasabov
    • 1
    • 2
  1. 1.KEDRIAuckland University of TechnologyNew Zealand
  2. 2.NICTJapan

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