Supervised Probabilistic Classification Based on Gaussian Copulas

  • Rogelio Salinas-Gutiérrez
  • Arturo Hernández-Aguirre
  • Mariano J. J. Rivera-Meraz
  • Enrique R. Villa-Diharce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)


This paper introduces copula functions and the use of the Gaussian copula function to model probabilistic dependencies in supervised classification tasks. A copula is a distribution function with the implicit capacity to model non linear dependencies via concordance measures, such as Kendall’s τ. Hence, this work studies the performance of a simple probabilistic classifier based on the Gaussian copula function. Without additional preprocessing of the source data, a supervised pixel classifier is tested with a 50-images benchmark; the experiments show this simple classifier has an excellent performance.


Gaussian copula supervised classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rogelio Salinas-Gutiérrez
    • 1
  • Arturo Hernández-Aguirre
    • 1
  • Mariano J. J. Rivera-Meraz
    • 1
  • Enrique R. Villa-Diharce
    • 1
  1. 1.Center for Research in Mathematics (CIMAT)GuanajuatoMéxico

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