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Soft Margin Trees

  • Jorge Díez
  • Juan José del Coz
  • Antonio Bahamonde
  • Oscar Luaces
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

From a multi-class learning task, in addition to a classifier, it is possible to infer some useful knowledge about the relationship between the classes involved. In this paper we propose a method to learn a hierarchical clustering of the set of classes. The usefulness of such clusterings has been exploited in bio-medical applications to find out relations between diseases or populations of animals. The method proposed here defines a distance between classes based on the margin maximization principle, and then builds the hierarchy using a linkage procedure. Moreover, to quantify the goodness of the hierarchies we define a measure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods.

Keywords

Support Vector Machine Hierarchical Cluster Agglomerative Hierarchical Cluster Hierarchical Cluster Algorithm Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jorge Díez
    • 1
  • Juan José del Coz
    • 1
  • Antonio Bahamonde
    • 1
  • Oscar Luaces
    • 1
  1. 1.Artificial Intelligence CenterUniversity of Oviedo at GijónAsturiasSpain

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