• Boosting: Foundations and Algorithms

    Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning) (Hardback) By (author) Robert E. Schapire, By (author) Yoav Freund

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    DescriptionBoosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." 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. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

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  • Full bibliographic data for Boosting

    Foundations and Algorithms
    Authors and contributors
    By (author) Robert E. Schapire, By (author) Yoav Freund
    Physical properties
    Format: Hardback
    Number of pages: 544
    Width: 180 mm
    Height: 229 mm
    Thickness: 30 mm
    Weight: 980 g
    ISBN 13: 9780262017183
    ISBN 10: 0262017180

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    Warengruppen-Systematik des deutschen Buchhandels: 16320
    B&T Modifier: Academic Level: 02
    BIC subject category V2: UMB
    B&T Modifier: Text Format: 06
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    B&T Merchandise Category: COM
    Ingram Subject Code: XG
    Libri: I-XG
    DC22: 006.3/1, 006.31
    BISAC V2.8: COM051300
    BIC subject category V2: UYQM
    BISAC V2.8: COM037000
    LC subject heading:
    DC23: 006.31
    LC classification: Q325.75 .S33 2012
    LC subject heading:
    Ingram Theme: ASPT/SCITAS
    Thema V1.0: UYQM, UMB
    Edition statement
    New ed.
    Illustrations note
    77 b&w illus.
    MIT Press Ltd
    Imprint name
    MIT Press
    Publication date
    08 June 2012
    Publication City/Country
    Cambridge, Mass.
    Author Information
    Robert E. Schapire is Professor of Computer Science at Princeton University. For their work on boosting, Freund and Schapire received both the Godel Prize in 2003 and the Kanellakis Theory and Practice Award in 2004. Yoav Freund is Professor of Computer Science at the University of California, San Diego. For their work on boosting, Freund and Schapire received both the Godel Prize in 2003 and the Kanellakis Theory and Practice Award in 2004.
    Review quote
    "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 learning..." -- The Bactra Review For those who wish to work in the area, it is a clear and insightful view of the subject that deserves a place in the canon of machine learning and on the shelves of those who study it. -- Giles Hooker Journal of the American Statistical Association