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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)
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)
There is a little problem with this book. But overall, it's ok.Published 14 months ago by Guibiao Xu
I can't believe other reviewers actually read this book. I'm disappointed by the content of the book. Read morePublished on April 22, 2013 by David Rolland
Was nice to download the ebook immediately as I bought it. I got what I expected, although I found it to be expensive compared to the paper edition.Published on February 7, 2013 by Karina Bunyik