Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de S, Luȡs M.A. Silva, Jorge M.F. Santos, Luȡs A. Alexandre.
Tipo de material: TextoSeries Studies in Computational Intelligence ; 420 | Studies in Computational Intelligence ; 420Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: XVIII, 262 p. online resourceTipo de contenido:- text
- computer
- online resource
- 9783642290299
- SpringerLink (Online service)
- 006.3 23
- Q342
Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.
This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multilayer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEElike concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.
ZDB-2-ENG
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