Minimum Error Entropy Classification

• 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: Engineering.; Artificial intelligence.; Statistical physics.; Dynamical systems.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Statistical Physics, Dynamical Systems and Complexity.
• Φυσική περιγραφή: XVIII, 262 p. online resource.
• ISBN: 9783642290299
• ISSN: 1860-949X ;

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.