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Minimum Error Entropy Classification

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Gebonden, 262 blz. | Engels
Springer Berlin Heidelberg | 2013e druk, 2012
ISBN13: 9783642290282
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Springer Berlin Heidelberg 2013e druk, 2012 9783642290282
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Samenvatting

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.

Specificaties

ISBN13:9783642290282
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:262
Uitgever:Springer Berlin Heidelberg
Druk:2013

Inhoudsopgave

Introduction.- Continuous Risk Functionals.- MEE with Continuous Errors.- MEE with Discrete Errors.- EE-Inspired Risks.- Applications.

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        Minimum Error Entropy Classification