Boosting
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 wea...
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| Главные авторы: | , |
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| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
The MIT Press
2022
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| Предметы: | |
| Online-ссылка: | ONIX_20220125_9780262301183_40 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869521929599188992 |
|---|---|
| author | Schapire, Robert E. Freund, Yoav |
| author_browse | Freund, Yoav Schapire, Robert E. |
| author_facet | Schapire, Robert E. Freund, Yoav |
| author_sort | Schapire, Robert E. |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-77893 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-778932024-04-14T10:27:40Z Boosting Schapire, Robert E. Freund, Yoav Artificial intelligence Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2022-01-25T15:08:15Z 2022-01-25T15:08:15Z 2012 book ONIX_20220125_9780262301183_40 9780262301183 9780262017183 https://directory.doabooks.org/handle/20.500.12854/77893 eng Adaptive Computation and Machine Learning series image/jpeg n/a http://mitpress.mit.edu/9780262017183 The MIT Press The MIT Press ae0cf962-f685-4933-93d1-916defa5123d 9780262301183 9780262017183 The MIT Press 544 Cambridge open access |
| spellingShingle | Artificial intelligence Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Schapire, Robert E. Freund, Yoav Boosting |
| title | Boosting |
| title_full | Boosting |
| title_fullStr | Boosting |
| title_full_unstemmed | Boosting |
| title_short | Boosting |
| title_sort | boosting |
| topic | Artificial intelligence Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Artificial intelligence Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20220125_9780262301183_40 |
| work_keys_str_mv | AT schapireroberte boosting AT freundyoav boosting |