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Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning series), by Robert E. Schapire, Yoav Freund
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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.
- Sales Rank: #1083842 in Books
- Brand: Brand: MIT Press
- Published on: 2012-05-18
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .94" w x 7.00" l, 2.19 pounds
- Binding: Hardcover
- 544 pages
- Used Book in Good Condition
Most helpful customer reviews
9 of 10 people found the following review helpful.
Best machine learning book I've read
By Quanyi
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.
4 of 4 people found the following review helpful.
Wonderfully written
By Pat Finder
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...
3 of 3 people found the following review helpful.
Masterpiece from two respectable pioneers in ML
By Jason W
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.
Being a constructive proof of the famous conjecture that weak learnability and strong learnability are in fact connected, boosting emerged as not only a theoretician's amusement, but an algorithm that practitioners can actually use in practice. In recent years researchers have published a ton of papers polishing the proofs behind boosting and refining the actual algorithm used in practice. There is no doubt at all that reading this book is going to give you a very solid foundation for further pursuing more advanced/recent treatments of boosting.
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