Minimum Error Entropy Classification

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Joaquim P. Marques de Sá, "Minimum Error Entropy Classification"
English | ISBN: 3642290280 | 2013 | 260 pages | PDF | 4 MB


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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Tags: Classification, Entropy, Minimum
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