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Feature-Based Dissimilarity Space Classification

  • Robert P. W. Duin
  • Marco Loog
  • Elżbieta Pȩkalska
  • David M. J. Tax
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6388)

Abstract

General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [1,2]. They often arise in problems in which direct comparisons of objects are made by computing pairwise distances between images, spectra, graphs or strings.

Dissimilarity-based classifiers can also be defined in vector spaces [3]. A large comparative study has not been undertaken so far. This paper compares dissimilarity-based classifiers with traditional feature-based classifiers, including linear and nonlinear SVMs, in the context of the ICPR 2010 Classifier Domains of Competence contest. It is concluded that the feature-based dissimilarity space classification performs similar or better than the linear and nonlinear SVMs, as averaged over all 301 datasets of the contest and in a large subset of its datasets. This indicates that these classifiers have their own domain of competence.

Keywords

Feature Space Dissimilarity Measure Linear Support Vector Machine Training Object Neighbor Rule 
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 2010

Authors and Affiliations

  • Robert P. W. Duin
    • 1
  • Marco Loog
    • 1
  • Elżbieta Pȩkalska
    • 2
  • David M. J. Tax
    • 1
  1. 1.Faculty of Electrical Engineering, Mathematics and Computer SciencesDelft University of TechnologyThe Netherlands
  2. 2.School of Computer ScienceUniversity of ManchesterUnited Kingdom

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