Ensemble methods : foundations and algorithms
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9781439830031
- 006.31 23 ZHO-E
- QA278.4 .Z47 2012
- BUS061000 | COM021030 | COM037000
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
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IIITD Reference | Computer Science and Engineering | REF 006.31 ZHO-E (Browse shelf(Opens below)) | Available | 003923 |
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REF 006.31 SUG-I Introduction to statistical machine learning | REF 006.31 SUT-R Reinforcement learning : | REF 006.31 VAP-N The nature of statistical learning theory | REF 006.31 ZHO-E Ensemble methods : | REF 006.312 DAS-E Experimental designs in data science with least resources | REF 006.312 HOL-D Data mining : foundations and intelligent paradigms : | REF 006.312 HOL-D Data mining : |
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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