Boosting : Foundations and Algorithms

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Tác giả: Yoav Freund, Robert E Schapire

Ngôn ngữ: eng

ISBN-13: 978-0262017183

ISBN-13: 978-0262301183

Ký hiệu phân loại: 006.31 Machine learning

Thông tin xuất bản: Cambridge The MIT Press 2012

Mô tả vật lý: 1 electronic resource (544 p.)

Bộ sưu tập: Tài liệu truy cập mở

ID: 226960

 An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. 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.
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