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Minimum Error Entropy Classification - Jorge M. F. Santos,Luís M. A. Silva,Luís A. Alexandre,Joaquim P. Marques de Sá

Anglų
2012-07-25
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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 inc ... Visas aprašymas

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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.

Daugiau informacijos

Autorius Jorge M. F. Santos, Luís M. A. Silva, Luís A. Alexandre, Joaquim P. Marques de Sá
Leidėjas Springer Berlin Heidelberg
Series Studies in Computational Intelligence
Išleidimo metai 2012
Viršelio tipas Kieti viršeliai
EAN 9783642290282
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127,04 € 169,38 €