Minimum Error Entropy Classification

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Marques de Sá, Joaquim P. (Autor), Silva, Luís M.A (Autor), Santos, Jorge M.F (Autor), Alexandre, Luís A. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Studies in Computational Intelligence, 420
Temas:
Computational intelligence.
Artificial intelligence.
System theory.
Mathematical physics.
Computational Intelligence.
Artificial Intelligence.
Complex Systems.
Theoretical, Mathematical and Computational Physics.
Acceso en línea:Texto Completo
Descripción
Sumario: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 multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.
Descripción Física:XVIII, 262 p. online resource.
ISBN:9783642290299
ISSN:1860-9503 ;

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