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
"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, UC 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
Robert E. Schapire is Principal Researcher at Microsoft Research in
New York City. For their work on boosting, Freund and Schapire
received both the G del 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 G del Prize in 2003 and the Kanellakis
Theory and Practice Award in 2004.
This excellent book is a mind-stretcher that should be read and
reread, even by nonspecialists.—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
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