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.
"synopsis" may belong to another edition of this title.
Robert E. Schapire is Principal Researcher at Microsoft Research in New York City. For their work on boosting, Freund and Schapire received both the Gödel Prize in 2003 and the Kanellakis Theory and Practice Award in 2004.
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 UniversityBoosting 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 UniversityAn 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, BerkeleyBoosting 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"About this title" may belong to another edition of this title.
FREE shipping within U.S.A.
Destination, rates & speedsSeller: SecondSale, Montgomery, IL, U.S.A.
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00083533438
Quantity: 1 available
Seller: Seattle Goodwill, Seattle, WA, U.S.A.
Condition: Good. May have some shelf-wear due to normal use. Your purchase funds free job training and education in the greater Seattle area. Thank you for supporting Goodwills nonprofit mission! Seller Inventory # 0KVOG200GECK_ns
Quantity: 1 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 20281694-n
Quantity: Over 20 available
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condition: New. Boosting: Foundations and Algorithms 1.86. Book. Seller Inventory # BBS-9780262526036
Quantity: 5 available
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Condition: New. Seller Inventory # ABLIING23Feb2215580084520
Quantity: Over 20 available
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9780262526036
Quantity: Over 20 available
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9780262526036
Quantity: Over 20 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In English. Seller Inventory # ria9780262526036_new
Quantity: Over 20 available
Seller: Russell Books, Victoria, BC, Canada
paperback. Condition: New. Illustrated. Special order direct from the distributor. Seller Inventory # ING9780262526036
Quantity: Over 20 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 20281694-n
Quantity: Over 20 available