Ensemble methods: foundations and algorithms
"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, su…
Read More ↓Title Statements
- Full title :Ensemble methods : foundations and algorithms / Zhi-Hua Zhou.
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"--
Read More ↓Edition Statements
- Publication Date:2012.
Publication Statements
- Publisher(s):CRC Press
- Place of Publication:Boca Raton, Fla
- Language:eng
- Publication Date:2012.
The Physical Object
- Description:1 ressource en ligne (i, 219 p.).
Edition Identifiers
- ISBN:9781439830055, 1439830053
- oclc:ocn835414816
- LC Classification:QA278.4
- Dewey Decimal:519.5/35
Table of Contents
Boosting -- Bagging -- Combination Methods -- Diversity -- Ensemble Pruning -- Clustering Ensembles -- Advanced Topics.
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