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Supervised Classification with Associative SOM

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Book cover Computational Methods in Neural Modeling (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

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Abstract

This paper presents an extension of the Self Organizing Map model called Associative SOM that is able to process different types of input data in separated data-paths. The ASOM model can easily deal with situations of incomplete data-patterns and incorporate class labels for supervisory purposes. The ASOM is successfully compared with Multilayer Perceptrons in the incremental classification of six erythemato-squamous diseases, where only partial data is available in successive steps.

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References

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Author information

Authors and Affiliations

  1. Institute Tecnológico de Arag’on, C/ María Luna n°6, 50005, Zaragoza, Spain

    Rafael del-Hoyo & Alvaro Marco

  2. Departamento de Electrónica y Comunicaciones, University of Zaragossa, C/ María Luna 1, 50005, Zaragoza, Spain

    David Buldain

Authors
  1. Rafael del-Hoyo
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  2. David Buldain
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  3. Alvaro Marco
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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del-Hoyo, R., Buldain, D., Marco, A. (2003). Supervised Classification with Associative SOM. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_43

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  • DOI: https://doi.org/10.1007/3-540-44868-3_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

  • eBook Packages: Springer Book Archive

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