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Document Analysis and Recognition

, Volume 6, Issue 1, pp 1–9 | Cite as

From data topology to a modular classifier

  • Abdellatif EnnajiEmail author
  • Arnaud Ribert
  • Yves Lecourtier
Original Paper

Abstract.

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given.

Keywords:

Neural networks Clustering Distributed and modular classification systems Learning Pattern recognition 

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Copyright information

© Springer-Verlag Berlin/Heidelberg 2003

Authors and Affiliations

  • Abdellatif Ennaji
    • 1
    Email author
  • Arnaud Ribert
    • 1
  • Yves Lecourtier
    • 1
  1. 1.P.S.I: Perception, System, Information LaboratoryUniversity of RouenMont Saint Aignan cedexFrance

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