//]]>
Item type | Location | Call Number | Status | Date Due |
---|---|---|---|---|
E-Book | AUM Main Library | 006.3 (Browse Shelf) | Not for loan |
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.
There are no comments for this item.