Minimum Error Entropy Classification
Language: English
Published by Springer Berlin Heidelberg, 2012
- Hardcover
- New
Condition: New
£ 80.65
Quantity: Over 20 available
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents data classification methodologies based on a minimum error entropy approachIncludes both theoretical results and applications to real world datasetsWritten by leading experts in the fieldThis book explains the minimum er.
Seller Inventory # 5055764
- Title
- Minimum Error Entropy Classification
- Author
- Joaquim P. Marques de Sá|Luís M.A. Silva|Jorge M.F. Santos|Luís A. Alexandre
- Publisher
- Springer Berlin Heidelberg
- Publication year
- 2012
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3642290280
- ISBN 13
- 9783642290282
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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