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Data Complexity Analysis for Classifier Combination

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Multiple Classifier Systems (MCS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2096))

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Abstract

Multiple classifier methods are effective solutions to difficult pattern recognition problems. However, empirical successes and failures have not been completely explained. Amid the excitement and confusion, uncertainty persists in the optimality of method choices for specific problems due to strong data dependences of classifier performance. In response to this, I propose that further exploration of the methodology be guided by detailed descriptions of geometrical characteristics of data and classifier models.

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Author information

Authors and Affiliations

  1. Bell Laboratories, Lucent Technologies, 700 Mountain Ave., 2C-425, Murray Hill, NJ, 07974, USA

    Tin Kam Ho

Authors
  1. Tin Kam Ho
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Editor information

Editors and Affiliations

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

    Josef Kittler

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

    Fabio Roli

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© 2001 Springer-Verlag Berlin Heidelberg

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Kam Ho, T. (2001). Data Complexity Analysis for Classifier Combination. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_6

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  • DOI: https://doi.org/10.1007/3-540-48219-9_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42284-6

  • Online ISBN: 978-3-540-48219-2

  • eBook Packages: Springer Book Archive

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