Quasi-optimum combination of multilayer perceptrons for adaptive multiclass pattern recognition

  • Alberto Ruiz García
  • Francisco J. Arcas Túnez
Neural Networks for Perception
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


Standard multiclass pattern recognition requires frequent re-learning stages when the set of categories of interest evolves in time. In order to minimize the computation costs of class incorporation and removal, we divide the global multiclass recognizer into a collection of class pairwise neural dichotomizers. When a new class appears, an adequate set of dichotomizers is created and trained to discriminate the new class from the rest. If a class disappears, its associated dichotomizers are eliminated In both cases previously learned knowledge is not disturbed. The properties of neural recognizers and pairwise modularization allow an analytic quasi-optimum method for combining network outputs to obtain the global multiclass response. An incremental and distributed pattern recognition architecture is presented and its performance experimentally evaluated, obtaining better error rates and learning times than conventional multiclass recognizers using similar resources. The design is highly parallel and asyncronous, adequate for dynamic real time applications.


pattern classification neural networks density estimation data fusion classifiers combination 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Alberto Ruiz García
    • 1
  • Francisco J. Arcas Túnez
    • 1
  1. 1.Departamento de Informática y Sistemas Facultad de InformáticaUniversidad de MurciaSpain

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