Minimum Error Entropy Classification (Studies in Computational Intelligence #420)
by Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos and Luís A. Alexandre
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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 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.
Details
- Publisher
- Springer Berlin Heidelberg
- Authors
- Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre
- Format
- Paperback
- Published
- Aug 9, 2014
- ISBN-13
- 9783642437427
- Pages
- 262
- Printing
- Printed on demand
Minimum Error Entropy Classification (Studies in Computational Intelligence #420) for groups: pricing
Volume pricing applies automatically in your cart. No codes, no account needed.
| Copies | Price per copy | You save |
|---|---|---|
| 25 – 49 | $96.79 | 12% off list |
| 50 – 99 | $95.69 | 13% off list |
| 100+ | $94.59 | 14% off list |
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