Minimum Error Entropy Classification Studies in Computational Intelligence

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{u2010}layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE{u2010}like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions Annotation 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 Front Matter....Pages 1-15 Introduction....Pages 1-11 Continuous Risk Functionals....Pages 13-39 MEE with Continuous Errors....Pages 41-91 MEE with Discrete Errors....Pages 93-120 EE-Inspired Risks....Pages 121-137 Applications....Pages 139-213 Back Matter....Pages 215-259