Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning Series)

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9780262526036: Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning Series)
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"This excellent book is a mind-stretcher that should be read and reread, even bynonspecialists." -- Computing Reviews "Boosting is, quite simply, one of the best-written books I've read on machine learning..." -- The Bactra Review For those who wish to work in the area, it is a clear and insightful view of the subject that deserves a place in the canon of machine learning and on the shelves of those who study it. -- Giles Hooker Journal of the American Statistical Association

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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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Robert E. Schapire, Yoav Freund
Editorial: MIT Press Ltd, United States (2014)
ISBN 10: 0262526034 ISBN 13: 9780262526036
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Descripción MIT Press Ltd, United States, 2014. Paperback. Estado de conservación: New. 218 x 170 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. Nº de ref. de la librería AAZ9780262526036

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Descripción MIT Press 2014-02-11, 2014. Estado de conservación: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. de la librería NU-GRD-05071692

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Descripción MIT Press, 2014. PAP. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería WM-9780262526036

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Descripción MIT Press Ltd 2014-02-11, Cambridge, Mass., 2014. paperback. Estado de conservación: New. Nº de ref. de la librería 9780262526036

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Descripción MIT Press Ltd. Paperback. Estado de conservación: 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. Nº de ref. de la librería B9780262526036

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Descripción MIT Press, 2014. Estado de conservación: New. book. Nº de ref. de la librería ria9780262526036_rkm

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Descripción Estado de conservación: New. Depending on your location, this item may ship from the US or UK. Nº de ref. de la librería 97802625260360000000

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Descripción The MIT Press, 2014. Paperback. Estado de conservación: New. book. Nº de ref. de la librería 0262526034

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