Boosting - Foundations and Algorithms

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9780262526036: Boosting - Foundations and Algorithms
Revue de presse :

"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 American Statistical Association

Robert Schapire and Yoav Freund made a huge impact in machine and statistical learning with their invention of boosting, which has survived the test of time. There have been lively discussions about alternative explanations of why it works so well, and the jury is still out. This well-balanced book from the 'masters' covers boosting from all points of view, and gives easy access to the wealth of research that this field has produced.
Trevor Hastie, Statistics Department, Stanford University

Boosting has provided a platform for thinking about and designing machine learning algorithms for over 20 years. The simple and elegant idea behind boosting is a 'Mirror of Erised' that researchers view from many different perspectives. This book beautifully ties together these views, using the same limpid style found in Robert Schapire and Yoav Freund's original research papers. It's an important resource for machine learning research.
John Lafferty, University of Chicago and Carnegie Mellon University

An outstanding text, which provides an authoritative, self-contained, broadly accessible and very readable treatment of boosting methods, a widely applied family of machine learning algorithms pioneered by the authors. It nicely covers the spectrum from theory through methodology to applications.
Peter Bartlett, University of California, Berkeley

Boosting is an amazing machine learning algorithm of 'intelligence' with much success in practice. It allows a weak learner to adapt to the data at hand and become 'strong'; it seamlessly integrates statistical estimation and computation. In this book, Robert Schapire and Yoav Freund, two inventors of the field, present multiple, fascinating views of boosting to explain why and how it works.

-- Bin Yu, University of California, Berkeley

Présentation de l'éditeur :

Boosting 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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Robert E. Schapire; Yoav Freund
Edité par The MIT Press, U.S.A. (2014)
ISBN 10 : 0262526034 ISBN 13 : 9780262526036
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Description du livre The MIT Press, U.S.A., 2014. Soft cover. État : New. N° de réf. du libraire 0046524

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Robert E. Schapire; Yoav Freund
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ISBN 10 : 0262526034 ISBN 13 : 9780262526036
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Description du livre MIT Press Ltd, United States, 2014. Paperback. État : New. 218 x 170 mm. Language: English . Brand New Book. Boosting 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. N° de réf. du libraire AAU9780262526036

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Robert E. Schapire; Yoav Freund
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ISBN 10 : 0262526034 ISBN 13 : 9780262526036
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Description du livre The MIT Press, 2014. État : New. Series: Adaptive Computation and Machine Learning Series. Num Pages: 544 pages, 77 b&w illus. BIC Classification: UMB; UYA; UYQM. Category: (P) Professional & Vocational. Dimension: 181 x 230 x 24. Weight in Grams: 854. . 2014. Paperback. . . . . . N° de réf. du libraire V9780262526036

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Robert E. Schapire; Yoav Freund
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Description du livre MIT Press Ltd. Paperback. État : new. BRAND NEW, Boosting: Foundations and Algorithms, Robert E. Schapire, Yoav Freund, Boosting 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. N° de réf. du libraire B9780262526036

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Robert E. Schapire; Yoav Freund
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Description du livre MIT Press, 2014. PAP. État : New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. N° de réf. du libraire BB-9780262526036

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Description du livre The MIT Press. État : New. Series: Adaptive Computation and Machine Learning Series. Num Pages: 544 pages, 77 b&w illus. BIC Classification: UMB; UYA; UYQM. Category: (P) Professional & Vocational. Dimension: 181 x 230 x 24. Weight in Grams: 854. . 2014. Paperback. . . . . Books ship from the US and Ireland. N° de réf. du libraire V9780262526036

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Robert E. Schapire; Yoav Freund
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Description du livre MIT Press Ltd, United States, 2014. Paperback. État : New. 218 x 170 mm. Language: English . Brand New Book. Boosting 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. N° de réf. du libraire AAU9780262526036

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Description du livre The MIT Press, 2014. État : New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: "Robert Schapire and Yoav Freund made a huge impact in machine and statisticallearning with their invention of boosting, which has survived the test of time. There have beenlively discussions about alternative explanations of why it works so well, and the jury is stillout. This well-balanced book from the 'masters' covers boosting from all points of view, and giveseasy access to the wealth of research that this field has produced." -- TrevorHastie , Statistics Department, Stanford University. N° de réf. du libraire ABE_book_new_0262526034

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Robert E. Schapire; Yoav Freund
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Description du livre MIT Press Ltd 2014-02-11, Cambridge, Mass., 2014. paperback. État : New. N° de réf. du libraire 9780262526036

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Robert E. Schapire; Yoav Freund
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Description du livre Paperback. État : New. Not Signed; Boosting 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 optimiz. book. N° de réf. du libraire ria9780262526036_rkm

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