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Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de S�a, Lu�is M.A. Silva, Jorge M.F. Santos, Lu�is A. Alexandre.

By: Marques de S�a, Joaquim P [author.].
Contributor(s): Silva, Lu�is M.A [author.] | Santos, Jorge M.F [author.] | Alexandre, Lu�is A [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 420Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XVIII, 262 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642290299.Subject(s): Engineering | Artificial intelligence | Statistical physics | Dynamical systems | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Statistical Physics, Dynamical Systems and ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.
In: Springer eBooksSummary: 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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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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