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An Empirical Evaluation of Common Vector Based Classification Methods and Some Extensions

  • Katerine Díaz-Chito
  • Francesc J. Ferri
  • Wladimiro Díaz-Villanueva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

An empirical evaluation of linear and kernel common vector based approaches has been considered in this work. Both versions are extended by considering directions (attributes) that carry out very little information as if they were null. Experiments on different kinds of data confirm that using this as a regularization parameter leads to usually better (and never worse) results than the basic algorithms.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Katerine Díaz-Chito
    • 1
  • Francesc J. Ferri
    • 1
  • Wladimiro Díaz-Villanueva
    • 1
  1. 1.Dept. InformàticaUniversitat de ValènciaSpain

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