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MEE with Continuous Errors

  • Joaquim P. Marques de Sá
  • Luís M. A. Silva
  • Jorge M. F. Santos
  • Luís A. Alexandre
Part of the Studies in Computational Intelligence book series (SCI, volume 420)

Abstract

The present chapter analyzes the behavior of classifiers characterized by continuous distributions of the errors, which are trained to minimize errorentropy functionals, namely the Shannon and Rényi’s quadratic entropies, presented in the preceding chapter. The analysis focus mainly the classifier problem (does the MEE solution correspond to the min P e solution?), but consistency and generalization issues are also addressed.

Keywords

Shannon Entropy Gradient Ascent Gaussian Input Quadratic Entropy Error Entropy 
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 Berlin Heidelberg 2013

Authors and Affiliations

  • Joaquim P. Marques de Sá
    • 1
  • Luís M. A. Silva
    • 2
  • Jorge M. F. Santos
    • 3
  • Luís A. Alexandre
    • 4
  1. 1.Divisão de Sinal e Imagem Campus FEUPINEB-Instituto de Engenharia BiomédicaPortoPortugal
  2. 2.Dept. of MathematicsUniv. de AveiroAveiroPortugal
  3. 3.Dept. of MathematicsISEP, School of Engineering Polytechnic of PortoPortoPortugal
  4. 4.Dept. of InformaticsUniv. Beira Interior IT - Instituto de TelecomunicaçõesCovilhãPortugal

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