Minimum Error Entropy Classification
Minimum Error Entropy Classification by Joaquim P. Marques de Sá , Luís M.A. Silva , Jorge M.F. Santos , Luís A. Alexandre
English | PDF | 2013 | 270 Pages | ISBN : 3642290280 | 7.9 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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