Ensemble methods
Zhou, Zhi-Hua, Ph. D.
Ensemble methods foundations and algorithms / [electronic resource] : Zhi-Hua Zhou. - Boca Raton, Fla. : CRC Press, 2012. - xiv, 222 p. : ill. - Chapman & Hall/CRC machine learning & pattern recognition series . - Chapman & Hall/CRC machine learning & pattern recognition series. .
"A Chapman & Hall book."
Includes bibliographical references (p. 187-218) and index.
1. Introduction -- 2. Boosting -- 3. Bagging -- 4. Combination methods -- 5. Diversity -- 6. Ensemble pruning -- 7. Clustering ensembles -- 8. Advanced topics.
"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"--
Mode of access: World Wide Web.
9781439830055 (ebook : PDF)
Multiple comparisons (Statistics)
Set theory.
Mathematical analysis.
Electronic books.
Ensemble methods foundations and algorithms / [electronic resource] : Zhi-Hua Zhou. - Boca Raton, Fla. : CRC Press, 2012. - xiv, 222 p. : ill. - Chapman & Hall/CRC machine learning & pattern recognition series . - Chapman & Hall/CRC machine learning & pattern recognition series. .
"A Chapman & Hall book."
Includes bibliographical references (p. 187-218) and index.
1. Introduction -- 2. Boosting -- 3. Bagging -- 4. Combination methods -- 5. Diversity -- 6. Ensemble pruning -- 7. Clustering ensembles -- 8. Advanced topics.
"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"--
Mode of access: World Wide Web.
9781439830055 (ebook : PDF)
Multiple comparisons (Statistics)
Set theory.
Mathematical analysis.
Electronic books.