Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre
- Sá, J. P. Marques de, 1946-
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
- Physical Description:
- XVIII, 262 pages 110 illustrations : digital
- Additional Creators:
- Silva, Luís M.A., Santos, Jorge M.F., Alexandre, Luís A., and SpringerLink (Online service)
- Studies in Computational Intelligence, 1860-949X ; 420
- Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications.
- 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.
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
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