Learning and Forgetting with Local Information of New Objects

  • Fernando D. Vázquez
  • J. Salvador Sánchez
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to incorporate new knowledge from the experience obtained when classifying new objects and correspondingly, to improve the efficiency of the system. We employ a stochastic rule for classifying and editing, along with a condensing algorithm based on local density to forget superfluous data (and control the sample size). The effectiveness of the algorithm is experimentally evaluated using a number of data sets taken from the UCI Machine Learning Database Repository.

Keywords

Learning Classification Forgetting Editing Condensing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fernando D. Vázquez
    • 1
  • J. Salvador Sánchez
    • 2
  • Filiberto Pla
    • 2
  1. 1.Centro de Reconocimiento de Patrones y Minería de DatosUniversidad de OrienteSantiago de CubaCuba
  2. 2.Dept. Llentguages i Sistemas InformàticsUniversitat Jaume ICastellóSpain

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