http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
"This comprehensive book presents an in-depth and systematic introduction to ensemble methods for researchers in machine learning, data mining, and related areas. It helps readers solve modem problems in machine learning using these methods. The author covers the spectrum of research in ensemble methods, including such famous methods as boosting, bagging, and rainforest, along with current directions and methods not sufficiently addressed in other books. Chapters explore cutting-edge topics, such as semi-supervised ensembles, cluster ensembles, and comprehensibility, as well as successful applications"-- Provided by publisher
1. Introduction -- 2. Boosting -- 3. Bagging -- 4. Combination methods -- 5. Diversity -- 6. Ensemble pruning -- 7. Clustering Ensemble -- 8. Advanced Topics
Includes bibliographical references (p. 187-218) and index
Chapman & Hall/CRC machine learning & pattern recognition series