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Boosting : foundations and algorithms

Author: Robert E Schapire; Yoav Freund
Publisher: Cambridge, MA : MIT Press, ©2012.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, c2012
(DLC) 2011038972
(OCoLC)758388404
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
OCLC Number: 794669892
Description: 1 online resource (xv, 526 p.) : ill.
Contents: 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.
Series Title: Adaptive computation and machine learning.
Responsibility: 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 Read more...

 
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