Minimum Error Entropy Classification

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 multilayer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEElike concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

Presents data classification methodologies based on a minimum error entropy approach Includes both theoretical results and applications to real world datasets Written by leading experts in the field

Inhalt
Introduction.- Continuous Risk Functionals.- MEE with Continuous Errors.- MEE with Discrete Errors.- EE-Inspired Risks.- Applications.