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Boosting : foundations and algorithms

Autor: Robert E Schapire; Yoav Freund
Editorial: Cambridge, MA : MIT Press, ©2012.
Serie: Adaptive computation and machine learning.
Edición/Formato:   Libro-e : Documento : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
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  Leer más
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Género/Forma: Electronic books
Formato físico adicional: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, ©2012
(DLC) 2011038972
(OCoLC)758388404
Tipo de material: Documento, Recurso en Internet
Tipo de documento Recurso internet, Archivo de computadora
Todos autores / colaboradores: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
Número OCLC: 794669892
Premios: Winner of Selected as a Best of 2012 by Computing Reviews 2012
Descripción: 1 online resource (xv, 526 pages) : illustrations.
Contenido: 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.
Título de la serie: Adaptive computation and machine learning.
Responsabilidad: Robert E. Schapire and Yoav Freund.

Resumen:

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 by nonspecialists. * <i>Computing Reviews Leer más

 
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