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Evolutionary Generation of Prototypes for a Learning Vector Quantization Classifier

  • L. P. Cordella
  • C. De Stefano
  • F. Fontanella
  • A. Marcelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

An evolutionary computation based algorithm for data classification is presented. The proposed algorithm refers to the learning vector quantization paradigm and is able to evolve sets of points in the feature space in order to find the class prototypes. The more remarkable feature of the devised approach is its ability to discover the right number of prototypes needed to perform the classification task without requiring any a priori knowledge on the properties of the data analyzed. The effectiveness of the approach has been tested on satellite images and the obtained results have been compared with those obtained by using other classifiers.

Keywords

Feature Space Recognition Rate Crossover Operator Near Neighbor Reference Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • L. P. Cordella
    • 1
  • C. De Stefano
    • 2
  • F. Fontanella
    • 1
  • A. Marcelli
    • 3
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell’Informazione e Matematica IndustrialeUniversità di CassinoCassino (FR)Italy
  3. 3.Dipartimento di Ingegneria dell’Informazione e Ingegneria ElettricaUniversità di SalernoFisciano (SA)Italy

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