Boosting foundations algorithms di schapire robert (17 risultati)

Paperback. Condizione: Very Good. Illustrated. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: very_good.

paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardback. Condizione: Good. 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.…

Hardback. Condizione: Good. 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.…

Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

paperback. Condizione: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In English.

Condizione: New. Series: Adaptive Computation and Machine Learning Series. Num Pages: 544 pages, 77 b&w illus. BIC Classification: UMB; UYA; UYQM. Category: (P) Professional & Vocational. Dimension: 181 x 230 x 24. Weight in Grams: 854. . 2014. Paperback. . . . .

Condizione: As New. Unread book in perfect condition.

Condizione: New. Series: Adaptive Computation and Machine Learning Series. Num Pages: 544 pages, 77 b&w illus. BIC Classification: UMB; UYA; UYQM. Category: (P) Professional & Vocational. Dimension: 181 x 230 x 24. Weight in Grams: 854. . 2014. Paperback. . . . . Books ship from the US and Ireland.

Paperback. Condizione: Brand New. 544 pages. 9.00x7.00x0.94 inches. In Stock.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Condizione: Neu. Boosting | Foundations and Algorithms | Robert E. Schapire (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2014 | MIT Press | EAN 9780262526036 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…