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Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning series) Hardcover – May 18, 2012

4.4 out of 5 stars 14 customer reviews

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Editorial Reviews

Review

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)

About the Author

Robert E. Schapire is Professor of Computer Science at Princeton University. 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 Gödel Prize in 2003 and the Kanellakis Theory and Practice Award in 2004.

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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 544 pages
  • Publisher: The MIT Press (May 18, 2012)
  • Language: English
  • ISBN-10: 0262017180
  • ISBN-13: 978-0262017183
  • Product Dimensions: 7 x 0.9 x 9 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #1,067,728 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
This book is written beautifully. Although the theory behind is so rich and multi-clue, it is very easy to read. The arrangement of chapters is also very considerate. You can read it by chapter order if you have limited machine learning research experience like me, or you can easily pick-up the most interesting chapter just from the table of contents.

This book is for the readers who want to understand and inspired by the rich theory behind boosting algorithm. Boosting procedure itself is quite easy to use. Just check wikipedia.
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Format: Hardcover Verified Purchase
Love the book. Some of the detailed mathematics and margin theory is outside my expertise, but the first chapter makes it worth all the trouble. I implemented the algorithm on page 5 and it is working fine.

I have had a copy out of the library, and finally ordered my own copy.

Chapter 1 is the best writing I've ever seen as an introduction to a technical book. It's a beautiful work of art.

Excuse me while I go read chapter 2 and on into Margin Theory...
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Format: Paperback
A book on boosting coming from its inventors... do I need to say more? I was a CS student at Princeton University, and I was once fortunate enough to take Prof. Schapire's course on theoretical machine learning. Prof. Schapire is an amazing teacher and researcher. When I was writing this review I tried to avoid any bias I may have because of my respect and personal admiration for him, and yet I have to say this book is simply a masterpiece.

Reading this book was simply enjoyable. It is very well structured. Every chapter illustrates a differing perspective on boosting (either theoretical or practical), and as a whole they offer a complete view on this fantastic algorithm/mechanism. Chapter 1 is already good enough for normal practitioners, and if you are fascinated by the incredible performance of boosting and want to know more, please keep on reading and I believe when you finish the last page you will feel like everything is so clear. Theorems are rigorously proved. Algorithms are unequivocally laid out. Theories and practices work in perfect concert. The second and third parts of the book were the most interesting to me. Three distinct justifications of boosting's effectiveness are beautifully illustrated, and even more awesome, these theoretical underpinnings are directly related to ways to generalize the basic AdaBoost to other classification scenarios e.g. how to incorporate probability outputs from base classifiers, how to derive new variants by changing the underlying optimization problem, how to naturally extend to multi-class or multi-label classification, etc.
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Format: Paperback Verified Purchase
It's a quite comprehensive book, describing lots of different ways to look at the AdaBoost family of algorithms. For all I can tell, the authors have collected all the state-of-the-art knowledge about boosting at the time the book was written, from the publications developed both by them and by the other people. So if you want to learn pretty much everything about boosting (up to the publication date), this is the book to read.

But be prepared that it's not a quick and casual introduction, it's a collection of the in-depth mathematical papers. It's a book that takes a very long time and much effort to read thoroughly and understand. You can skim over the proofs but it still takes a long time, after all it's pretty much everything known about boosting. I actually highly recommend not spending too much time on the proofs when you read it for the first time. This will give you a good overall picture, and then if you want to go deeper, read the book for the second time, the mathematics will make more sense on the second reading. You can also skip chapters depending on your interests, if you're not out to learn everything about boosting.

Probably the only really annoying thing from the engineering standpoint is that the algorithms in the book are what the mathematicians and the fans of functional programming call "algorithms", not the algorithms in the normal engineering sense. It takes some deciphering to turn them into a straightforward readable and understandable form. Bug again, it's a book about math, not engineering. I've had some of the deciphering I've done posted to a blog but I probably can't post a link to it here.
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Format: Paperback Verified Purchase
It's a kind of problem I come across way too often: in trying to determine whether a specific condition exists, a number of symptoms might help the diagnosis. But, some of the symptoms can also appear when something else is going on, instead. On the other hand, not all of the symptoms necessarily appear when the condition in fact is active. The question then becomes, given a number of indicators that have some diagnostic value, and given that all of them are inaccurate some of the time, how do I combine the indicators' answers to get the best diagnosis of that condition?

This book proposes "Boosting" as the answer. Start with remarkably few technical requirements on the diagnostic indicators, plus some number of cases in which the condition's presence or absence is already known. Given that, Boosting iteratively determines the weight to assign each indicator. One requirement is that each indicator suggests presence or absence of the condition at least somewhat differently than random guessing - and being wrong most of the time is just as useful as being right more often than not, since the algorithm automatically assigns negative weights to such indicators.

After that, the authors present rigorous development in a number of directions. I emphasize "rigor" - this text offers detailed development, analysis, and formal proof of the algorithm and its properties, far beyond the needs of someone who just wants to implement the technique. Implementable detail is there, but you'll spend a fair bit of time teasing it out of the dense notation used here.

Then, once basics have been established, the discussion branches out. The authors offer game-theoretic analysis of the algorithm, along with comparisons to related optimization techniques.
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