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Taxonomy of Classifiers Based on Dissimilarity Features

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Book cover Pattern Recognition and Data Mining (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3686))

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Abstract

A great number of linear and nonlinear classification algorithms can follow from a general representation of discriminant function written as a weighted sum of kernel functions of dissimilarity features. Unified look at the algorithms allows obtaining intermediate classifiers which after tuning of the weights and other model’s parameters can outperform popular dissimilarity based methods. Simulations with artificial and real world data sets revealed efficiency of single layer perceptron trained in a special way.

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Author information

Authors and Affiliations

  1. Institute of Mathematics and Informatics, Akademijos st. 4, Vilnius, 08663, Lithuania

    Sarunas Raudys

Authors
  1. Sarunas Raudys
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Editor information

Editors and Affiliations

  1. Research School of Infomatics, Loughborough, UK

    Sameer Singh

  2. ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK

    Maneesha Singh

  3. IBM Corporation, 1133 Wetchester Avenue, White Plains, 10604, New York, United States

    Chid Apte

  4. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

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© 2005 Springer-Verlag Berlin Heidelberg

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Raudys, S. (2005). Taxonomy of Classifiers Based on Dissimilarity Features. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_15

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  • DOI: https://doi.org/10.1007/11551188_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28757-5

  • Online ISBN: 978-3-540-28758-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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