Boosting is a very useful machine learning method based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb."
We are pleased to announce the publication of our new book,
"Boosting: Foundations and Algorithms."
For more details, see the MIT Press website at:
The book is also available from the usual outlets, such as:
Rob Schapire & Yoav Freund
From the book overview:
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.