Minimum Error Entropy Classification
Marques de Sá, Joaquim P.
Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre. - XVIII, 262 p. online resource. - Studies in Computational Intelligence, 420 1860-949X ; . - Studies in Computational Intelligence, 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.
9783642290299
10.1007/978-3-642-29029-9 doi
Engineering.
Artificial intelligence.
Statistical physics.
Dynamical systems.
Computational intelligence.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
Statistical Physics, Dynamical Systems and Complexity.
Q342
006.3
Minimum Error Entropy Classification [electronic resource] / by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre. - XVIII, 262 p. online resource. - Studies in Computational Intelligence, 420 1860-949X ; . - Studies in Computational Intelligence, 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.
9783642290299
10.1007/978-3-642-29029-9 doi
Engineering.
Artificial intelligence.
Statistical physics.
Dynamical systems.
Computational intelligence.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
Statistical Physics, Dynamical Systems and Complexity.
Q342
006.3