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Minimum Error Entropy Classification

by Marques de Sá, Joaquim P.
Authors: Silva, Luís M.A.%author. | Santos, Jorge M.F.%author. | Alexandre, Luís A.%author. | SpringerLink (Online service) Series: Studies in Computational Intelligence, 1860-949X ; . 420 Physical details: XVIII, 262 p. 110 illus. online resource. ISBN: 3642290299 Subject(s): Engineering. | Artificial intelligence. | Engineering. | Computational Intelligence. | Artificial Intelligence (incl. Robotics). | Statistical Physics, Dynamical Systems and Complexity.
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E-Book E-Book AUM Main Library 006.3 (Browse Shelf) Not for loan

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 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.

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