Minimum Error Entropy Classification - Jorge M. F. Santos,Luís M. A. Silva,Luís A. Alexandre,Joaquim P. Marques de Sá
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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 inc ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Jorge M. F. Santos, Luís M. A. Silva, Luís A. Alexandre, Joaquim P. Marques de Sá |
|---|---|
| Izdevējs | Springer Berlin Heidelberg |
| Series | Studies in Computational Intelligence |
| Izlaides gads | 2012 |
| Vāka tips | Cietais vāks |
| EAN | 9783642290282 |