ENGLISH

Minimum error entropy classification

By Joaquim P. Marques de Sá , Luís M.A. Silva , Jorge M.F. Santos , Luís A. Alexandre (auth.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2013
ISBN
978-3-642-29028-2, 978-3-642-29029-9
DOI
10.1007/978-3-642-29029-9
Language
english
Format
PDF
Filesize
3 MB (3233167 bytes)
Series
Studies in Computational Intelligence 420
Edition
1
Pages
262\270
Library
Kolxo3
Time added
2013-12-29 19:00:00
Computational Intelligence;Artificial IntelligenceDynamical Systems and Complexity

Description

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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