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Fast Feature Selection in an HMM-Based Multiple Classifier System for Handwriting Recognition

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Pattern Recognition (DAGM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

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Abstract

A novel, fast feature selection method for hidden Markov model (HMM) based classifiers is introduced in this paper. It is also shown how this method can be used to create ensembles of classifiers. The proposed methods are tested in the context of a handwritten text recognition task.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bern, Neubrückstrasse 10, CH-3012, Bern, Switzerland

    Simon Günter & Horst Bunke

Authors
  1. Simon Günter
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  2. Horst Bunke
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Editor information

Editors and Affiliations

  1. Otto-von-Guericke University, 39106, Magdeburg, Germany

    Bernd Michaelis

  2. Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Gerald Krell

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© 2003 Springer-Verlag Berlin Heidelberg

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Günter, S., Bunke, H. (2003). Fast Feature Selection in an HMM-Based Multiple Classifier System for Handwriting Recognition. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_38

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  • DOI: https://doi.org/10.1007/978-3-540-45243-0_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

  • eBook Packages: Springer Book Archive

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