Buch, Englisch, 262 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 5443 g
Buch, Englisch, 262 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 5443 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-642-29028-2
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Introduction.- Continuous Risk Functionals.- MEE with Continuous Errors.- MEE with Discrete Errors.- EE-Inspired Risks.- Applications.
