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On Combining Dissimilarity Representations

  • Elżbieta Pękalska
  • Robert P. W. Duin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Abstract

For learning purposes, representations of real world objects can be built by using the concept of dissimilarity (distance). In such a case, an object is characterized in a relative way, i.e. by its dissimilarities to a set of the selected prototypes. Such dissimilarity representations are found to be more practical for some pattern recognition problems.

When experts cannot decide for a single dissimilarity measure, a number of them may be studied in parallel. We investigate two possibilities of combining either dissimilarity representations themselves or classifiers built on each of them separately. Our experiments conducted on a handwritten digit set demonstrate that when the dissimilarity representations are of different nature, a much better performance can be obtained by their combination than on individual representations.

Keywords

Product Rule Near Neighbor Error Curve Handwritten Digit Combine Representation 
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 2001

Authors and Affiliations

  • Elżbieta Pękalska
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Pattern Recognition Group, Department of Applied Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands

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