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Ensemble methods : foundations and algorithms

By: Zhou, Zhi-Hua, Ph. D.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC machine learning & pattern recognition series.Publisher: Boca Raton, FL : Taylor & Francis, 2012Description: xiv, 222 p. : ill. ; 25 cm.ISBN: 9781439830031.Subject(s): Multiple comparisons (Statistics) | Set theory | Mathematical analysis | BUSINESS & ECONOMICS / Statistics | COMPUTERS / Database Management / Data Mining | COMPUTERS / Machine TheorySummary: "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"--
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Computer Science and Engineering REF 006.31 ZHO-E (Browse shelf) Checked out 03/05/2017 003923
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Includes bibliographical references (p. 187-218) and index.

"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"--

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