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Minimum Error Entropy Classification
| Content Provider | Springer-eBooks |
|---|---|
| Author | Marques De Sá, Joaquim P. Silva, Luís M. A. Santos, Jorge M. F. Alexandre, Luís A. |
| Copyright Year | 2013 |
| Abstract | 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. |
| File Format | |
| ISBN | 9783642290299 |
| Language | English |
| Publisher | SpringerLink Springer eBooks |
| Access Restriction | Subscribed |
| Subject Keyword | Engineering Computational Intelligence Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity |
| Content Type | Text |
| Resource Type | Book |
