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Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition

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Multiple Classifier Systems (MCS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3077))

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Abstract

There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the following two approaches is used to create a multiple classifier system: 1. Several classifiers are developed completely independent of each other and combined in a last step. 2. Several classifiers are created out of one prototype classifier by using so called classifier ensemble methods. In this paper a novel algorithm which combines both approaches is introduced. This new algorithm is experimentally evaluated in the context of hidden Markov model (HMM) based handwritten word recognizers and compared to previously introduced methods which also combine both approaches.

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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. Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, 09123, Cagliari, Italy

    Fabio Roli

  2. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Josef Kittler

  3. Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, GU2 7XH, Guildford, Surrey, United Kingdom

    Terry Windeatt

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© 2004 Springer-Verlag Berlin Heidelberg

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Günter, S., Bunke, H. (2004). Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_31

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  • DOI: https://doi.org/10.1007/978-3-540-25966-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22144-9

  • Online ISBN: 978-3-540-25966-4

  • eBook Packages: Springer Book Archive

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