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Ensemble methods : foundations and algorithms

By: Material type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublication details: Boca Raton, FL : Taylor & Francis, ©2012.Description: xiv, 222 p. : ill. ; 25 cmISBN:
  • 9781439830031
Subject(s): DDC classification:
  • 006.31 23 ZHO-E
LOC classification:
  • QA278.4 .Z47 2012
Other classification:
  • BUS061000 | COM021030 | COM037000
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"--
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Item type Current library Collection Call number Status Date due Barcode Item holds
Books Books IIITD Reference Computer Science and Engineering REF 006.31 ZHO-E (Browse shelf(Opens below)) Available 003923
Total holds: 0

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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