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Modeling Dependencies in Supervised Classification

  • Rogelio Salinas-GutiérrezEmail author
  • Angélica Hernández-Quintero
  • Oscar Dalmau-Cedeño
  • Ángela Paulina Pérez-Díaz
Conference paper
  • 909 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

In this paper we show the advantage of modeling dependencies in supervised classification. The dependencies among variables in a multivariate data set can be linear or non linear. For this reason, it is important to consider flexible tools for modeling such dependencies. Copula functions are able to model different kinds of dependence structures. These copulas were studied and applied in classification of pixels. The results show that the performance of classifiers is improved when using copula functions.

Keywords

Copula function Graphical model Likelihood function 

Notes

Acknowledgments

The authors acknowledge the financial support from the National Council of Science and Technology of México (CONACyT, grant number 258033) and from the Universidad Autónoma de Aguascalientes (project number PIM17-3). The student Ángela Paulina also acknowledges to the CONACYT for the financial support given through the scholarship number 628293.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rogelio Salinas-Gutiérrez
    • 1
    Email author
  • Angélica Hernández-Quintero
    • 1
  • Oscar Dalmau-Cedeño
    • 2
  • Ángela Paulina Pérez-Díaz
    • 1
  1. 1.Universidad Autónoma de AguascalientesAguascalientesMexico
  2. 2.Centro de Investigación en MatemáticasGuanajuatoMexico

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