Minimum Error Entropy Classification

ISBN-13: 9783642290282 / Angielski / Twarda / 2012 / 262 str.

Joaquim Marques De S.; Lu?'s Miguel Almeida Da Silva; Jorge M. Santos

Opis

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.

Recenzje

From the reviews:

"The paper deals with the theoretical background and corresponding applications of minimum error entropy (MEE) to different data classifications models ... . Many examples and tests are also provided to illustrate the practical application of MEE in concrete classification problems. The book is dedicated to researchers and practitioners working on machine learning algorithms interested in using MEE in data classification." (Florin Gorunescu, zbMATH, Vol. 1280, 2014)

Spis treści

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

O autorze

Luís Silva, anthropologist, is Post-Doctoral research fellow at the Centre for Research in Anthropology, Universidade Nova de Lisboa (CRIA/FCSHUNL), Lisbon, Portugal. His principal research interests include rural dynamics and the anthropology of tourism, focusing specifically on the making of heritage and tourism products in rural areas, as well as on the local impact of tourism and the heritage enterprise.