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Boosting : foundations and algorithms

作者: Robert E Schapire; Yoav Freund
出版商: Cambridge, MA : MIT Press, ©2012.
叢書: Adaptive computation and machine learning.
版本/格式:   電子書 : 文獻 : 英語所有版本和格式的總覽
資料庫:WorldCat
提要:
A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by  再讀一些...
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類型/形式: Electronic books
其他的實體格式: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, c2012
(DLC) 2011038972
(OCoLC)758388404
資料類型: 文獻, 網際網路資源
文件類型: 網路資源, 電腦資料
所有的作者/貢獻者: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
OCLC系統控制編碼: 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.
叢書名: Adaptive computation and machine learning.
責任: Robert E. Schapire and Yoav Freund.

摘要:

A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. --

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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 再讀一些...

 
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