Some Experiments in Supervised Pattern Recognition with Incomplete Training Samples

  • Ricardo Barandela
  • Francesc J. Ferri
  • Tania Nájera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

This paper presents some ideas about automatic procedures to implement a system with the capability of detecting patterns arising from classes not represented in the training sample. The procedure aims at incorporating automatically to the training sample the necessary information about the new class for correctly recognizing patterns from this class in future classification tasks. The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the peril of incorporating noisy data to the training sample. Experimental results with real data confirm the benefits of the proposed procedure.

Keywords

Training Sample Outlier Detection Near Neighbor Training Pattern Generalize Edition 
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 2002

Authors and Affiliations

  • Ricardo Barandela
    • 1
  • Francesc J. Ferri
    • 2
  • Tania Nájera
    • 1
  1. 1.Lab for Pattern RecognitionInstituto Tecnológico de Toluca, MéxicoMetepecEstado de México
  2. 2.Dept. d’InformaticaUniversitat de ValènciaBurjassot, ValènciaSpain

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