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Boosting : foundations and algorithms

著者: Robert E Schapire; Yoav Freund
出版商: Cambridge, MA : MIT Press, ©2012.
丛书: Adaptive computation and machine learning.
版本/格式:   电子图书 : 文献 : 英语查看所有的版本和格式
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类型/形式: Electronic books
附加的形体格式: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, c2012
(DLC) 2011038972
(OCoLC)758388404
材料类型: 文献, 互联网资源
文件类型: 互联网资源, 计算机文档
所有的著者/提供者: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
OCLC号码: 794669892
描述: 1 online resource (xv, 526 p.) : ill.
内容: Foundations of machine learning --
Using AdaBoost to minimize training error --
Direct bounds on the generalization error --
The margins explanation for boosting's effectiveness --
Game theory, online learning, and boosting --
Loss minimization and generalizations of boosting --
Boosting, convex optimization, and information geometry --
Using confidence-rated weak predictions --
Multiclass classification problems --
Learning to rank --
Attaining the best possible accuracy --
Optimally efficient boosting --
Boosting in continuous time.
丛书名: Adaptive computation and machine learning.
责任: Robert E. Schapire and Yoav Freund.

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"This excellent book is a mind-stretcher that should be read and reread, even bynonspecialists." -- Computing Reviews "Boosting is, quite simply, one of the best-written books I've read on machine 再读一些...

 
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