Minimum Error Entropy Classification (Paperback, 2013 ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Computational Intelligence, 420
Release date:	August 2014
First published:	2013
Authors:	Joaquim P. Marques De Sa • Luis M.A. Silva • Jorge M.F. Santos • Luis A. Alexandre
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	262
Edition:	2013 ed.
ISBN-13:	978-3-642-43742-7
Categories:	Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-43742-7
Barcode:	9783642437427

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