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Recursive Adaptive ECOC models

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Progress in Artificial Intelligence (EPIA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2258))

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Abstract

A general framework for the design of error adaptive learning algorithms in multiple output domains based on Dietterich’s ECOC approach, recursive error output correcting codes and iterative APP decoding methods is proposed. A particular class of these Recursive ECOC (RECOC) learning algorithms based on Low Density Parity Check is presented.

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References

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Author information

Authors and Affiliations

  1. Department of Telematics Engineering, Technical University of Madrid, Spain

    Elizabeth Tapia, José Carlos González & Julio Villena

  2. Department of Informatic Systems, Complutense University of Madrid, Spain

    Javier García-Villalba

Authors
  1. Elizabeth Tapia
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  2. José Carlos González
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  3. Javier García-Villalba
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  4. Julio Villena
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Editor information

Editors and Affiliations

  1. Faculty of Economics LIACC, Laboratório de Inteligência Artificial e Ciência de Computadores, University of Porto, Rua do Campo Alegre, 823, 4150-180, Porto, Portugal

    Pavel Brazdil  & Alípio Jorge  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Tapia, E., González, J.C., García-Villalba, J., Villena, J. (2001). Recursive Adaptive ECOC models. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_13

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  • DOI: https://doi.org/10.1007/3-540-45329-6_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43030-8

  • Online ISBN: 978-3-540-45329-1

  • eBook Packages: Springer Book Archive

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