Minimum Error Entropy Classification

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Marques de Sá, Joaquim P. (Συγγραφέας), Silva, Luís M.A (Συγγραφέας), Santos, Jorge M.F (Συγγραφέας), Alexandre, Luís A. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Σειρά:Studies in Computational Intelligence, 420
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Marques de Sá, Joaquim P.  |e author. 
245 1 0 |a Minimum Error Entropy Classification  |h [electronic resource] /  |c by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2013. 
300 |a XVIII, 262 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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338 |a online resource  |b cr  |2 rdacarrier 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 420 
505 0 |a Introduction -- Continuous Risk Functionals -- MEE with Continuous Errors -- MEE with Discrete Errors -- EE-Inspired Risks -- Applications. 
520 |a 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. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Statistical physics. 
650 0 |a Dynamical systems. 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Statistical Physics, Dynamical Systems and Complexity. 
700 1 |a Silva, Luís M.A.  |e author. 
700 1 |a Santos, Jorge M.F.  |e author. 
700 1 |a Alexandre, Luís A.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642290282 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 420 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-29029-9  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)