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Using the Geometrical Distribution of Prototypes for Training Set Condensing

  • María Teresa Lozano
  • José Salvador Sánchez
  • Filiberto Pla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3040)

Abstract

In this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithms proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing the reduction rate and the classification accuracy with those of other condensing techniques.

Keywords

Voronoi Diagram Near Neighbour Decision Boundary Geometrical Distribution Pattern Recognition Letter 
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 2004

Authors and Affiliations

  • María Teresa Lozano
    • 1
  • José Salvador Sánchez
    • 1
  • Filiberto Pla
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosUniversitat Jaume I, Campus Riu SecCastellónSpain

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