Boosting: Foundations and Algorithms

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9780262017183: 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. 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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Schapire, Robert E.; Freund, Yoav
Editore: MIT Press Ltd, United States (2012)
ISBN 10: 0262017180 ISBN 13: 9780262017183
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Descrizione libro MIT Press Ltd, United States, 2012. Hardback. Condizione libro: New. 229 x 180 mm. Language: English . Brand New Book. 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. Codice libro della libreria AAZ9780262017183

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Schapire, Robert E.; Freund, Yoav
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Descrizione libro MIT Press, 2012. HRD. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria GB-9780262017183

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Schapire, Robert E.; Freund, Yoav
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Descrizione libro MIT Press Ltd 2012-06-08, Cambridge, Mass., 2012. hardback. Condizione libro: New. Codice libro della libreria 9780262017183

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Schapire, Robert E.; Freund, Yoav
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Descrizione libro The MIT Press, 2012. Condizione libro: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: "Boosting is an amazing machine learning algorithm of 'intelligence' with muchsuccess in practice. It allows a weak learner to adapt to the data at hand and become 'strong'; itseamlessly integrates statistical estimation and computation. In this book, Robert Schapire and YoavFreund, two inventors of the field, present multiple, fascinating views of boosting to explain whyand how it works." -- Bin Yu , University of California, Berkeley. Codice libro della libreria ABE_book_new_0262017180

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Schapire, Robert E.; Freund, Yoav
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ISBN 10: 0262017180 ISBN 13: 9780262017183
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Descrizione libro MIT Press Ltd, United States, 2012. Hardback. Condizione libro: New. 229 x 180 mm. Language: English . Brand New Book. 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. Codice libro della libreria AAZ9780262017183

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Descrizione libro Condizione libro: New. Bookseller Inventory # ST0262017180. Codice libro della libreria ST0262017180

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Descrizione libro Hardback. Condizione libro: New. Not Signed; 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 optimiz. book. Codice libro della libreria ria9780262017183_rkm

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Descrizione libro MIT Press, 2012. Hardcover. Condizione libro: New. Brand New Book. Shipping: Once your order has been confirmed and payment received, your order will then be processed. The book will be located by our staff, packaged and despatched to you as quickly as possible. From time to time, items get mislaid en route. If your item fails to arrive, please contact us first. We will endeavour to trace the item for you and where necessary, replace or refund the item. Please do not leave negative feedback without contacting us first. All orders will be dispatched within two working days. If you have any quesions please contact us. Codice libro della libreria V9780262017183

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Schapire, Robert E.; Freund, Yoav
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Descrizione libro MIT Press Ltd, 2012. Condizione libro: New. 2012. Hardcover. Series: Adaptive Computation and Machine Learning Series. Num Pages: 544 pages, 77 b&w illus. BIC Classification: UMB; UYQM. Category: (P) Professional & Vocational. Dimension: 234 x 185 x 30. Weight in Grams: 1008. . . . . . . Codice libro della libreria V9780262017183

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Descrizione libro MIT Press Ltd. Hardback. Condizione libro: new. BRAND NEW, Boosting: Foundations and Algorithms, Robert E. Schapire, Yoav Freund, 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. Codice libro della libreria B9780262017183

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