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Boosting : foundations and algorithms

著者: Robert E Schapire; Yoav Freund
出版: Cambridge, MA : MIT Press, ©2012.
シリーズ: Adaptive computation and machine learning.
エディション/フォーマット:   電子書籍 : Document : Englishすべてのエディションとフォーマットを見る
データベース:WorldCat
概要:
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  続きを読む
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ジャンル/形式: Electronic books
その他のフォーマット: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, c2012
(DLC) 2011038972
(OCoLC)758388404
資料の種類: Document, インターネット資料
ドキュメントの種類: インターネットリソース, コンピューターファイル
すべての著者/寄与者: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
OCLC No.: 794669892
物理形態: 1 online resource (xv, 526 p.) : ill.
コンテンツ: 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.
シリーズタイトル: Adaptive computation and machine learning.
責任者: Robert E. Schapire and Yoav Freund.

概要:

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 続きを読む

 
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