Boosting - Foundations and Algorithms

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.

Info autore

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.

Dettagli sul prodotto

• Autori: Yoav Freund, Yoav (Professor Freund, Robert E. Schapire, Robert E. (Principal Researcher Schapire, Robert E. Freund Schapire, Robert E./ Freund Schapire
• Con la collaborazione di: Francis (INRIA - Willow Project-Team) Bach (Editore)
• Editore: The MIT Press
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 18.05.2012
• EAN: 9780262017183
• ISBN: 978-0-262-01718-3
• Pagine: 544
• Serie: Adaptive Computation and Machine Learning series; Adaptive Computation and Machi; Boosting; Boosting
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP