Minimum Error Entropy Classification

This book explains the minimum error entropy (MEE) concept applied to data classification machines. Discusses theoretical results, offers a clustering algorithm using a MEE-like concept, and includes tests, evaluation experiments and comparative applications.

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

Autorentext
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.

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