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Ensemble methods [electronic resource] : foundations and algorithms / Zhi-Hua Zhou.

By: Material type: TextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublication details: Boca Raton, Fla. : CRC Press, 2012.Description: xiv, 222 p. : illISBN:
  • 9781439830055 (ebook : PDF)
Subject(s): Genre/Form: Additional physical formats: No titleOnline resources: Available additional physical forms:
  • Also available in print edition.
Contents:
1. Introduction -- 2. Boosting -- 3. Bagging -- 4. Combination methods -- 5. Diversity -- 6. Ensemble pruning -- 7. Clustering ensembles -- 8. Advanced topics.
Summary: "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.
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"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"-- Provided by publisher.

Also available in print edition.

Mode of access: World Wide Web.

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