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, conv...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schapire, Robert E. [verfasserIn] Freund, Yoav [mitwirkender] |
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| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts: MIT Press ; c2012 |
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| Schlagwörter: |
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| Anmerkung: |
Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 |
|---|---|
| Umfang: |
1 PDF (xv, 526 pages) ; illustrations. |
| Beschreibung: |
Mode of access: World Wide Web. |
| Reihe: |
Adaptive computation and machine learning series |
|---|
| Links: | |
|---|---|
| ISBN: |
978-0-262-30118-3 |
| Katalog-ID: |
1727349008 |
|---|
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| allfields |
9780262301183 electronic book 978-0-262-30118-3 (DE-627)1727349008 (DE-599)KEP055120547 (OCoLC)857969078 (ZBM)1278.68021 (MITPRESS)6267536 (EBP)055120547 DE-627 ger DE-627 rda eng *68-02 msc 68T05 msc 62H30 msc Schapire, Robert E. verfasserin aut Boosting foundations and algorithms Robert E. Schapire and Yoav Freund Cambridge, Massachusetts MIT Press c2012 1 PDF (xv, 526 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Boosting (Algorithms) Supervised learning (Machine learning) Freund, Yoav mitwirkender ctb 9780262017183 Erscheint auch als Druck-Ausgabe 9780262017183 https://ieeexplore.ieee.org/book/6267536 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1278.68021 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848475081 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240581 olr-MIT i z 23-07-21 100 01 3100 4472467690 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221996 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740750375 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267536 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267536 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267536 100 01 3100 https://ieeexplore.ieee.org/book/6267536 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267536 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267536 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| spelling |
9780262301183 electronic book 978-0-262-30118-3 (DE-627)1727349008 (DE-599)KEP055120547 (OCoLC)857969078 (ZBM)1278.68021 (MITPRESS)6267536 (EBP)055120547 DE-627 ger DE-627 rda eng *68-02 msc 68T05 msc 62H30 msc Schapire, Robert E. verfasserin aut Boosting foundations and algorithms Robert E. Schapire and Yoav Freund Cambridge, Massachusetts MIT Press c2012 1 PDF (xv, 526 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Boosting (Algorithms) Supervised learning (Machine learning) Freund, Yoav mitwirkender ctb 9780262017183 Erscheint auch als Druck-Ausgabe 9780262017183 https://ieeexplore.ieee.org/book/6267536 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1278.68021 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848475081 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240581 olr-MIT i z 23-07-21 100 01 3100 4472467690 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221996 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740750375 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267536 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267536 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267536 100 01 3100 https://ieeexplore.ieee.org/book/6267536 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267536 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267536 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| allfields_unstemmed |
9780262301183 electronic book 978-0-262-30118-3 (DE-627)1727349008 (DE-599)KEP055120547 (OCoLC)857969078 (ZBM)1278.68021 (MITPRESS)6267536 (EBP)055120547 DE-627 ger DE-627 rda eng *68-02 msc 68T05 msc 62H30 msc Schapire, Robert E. verfasserin aut Boosting foundations and algorithms Robert E. Schapire and Yoav Freund Cambridge, Massachusetts MIT Press c2012 1 PDF (xv, 526 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Boosting (Algorithms) Supervised learning (Machine learning) Freund, Yoav mitwirkender ctb 9780262017183 Erscheint auch als Druck-Ausgabe 9780262017183 https://ieeexplore.ieee.org/book/6267536 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1278.68021 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848475081 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240581 olr-MIT i z 23-07-21 100 01 3100 4472467690 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221996 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740750375 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267536 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267536 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267536 100 01 3100 https://ieeexplore.ieee.org/book/6267536 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267536 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267536 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| allfieldsGer |
9780262301183 electronic book 978-0-262-30118-3 (DE-627)1727349008 (DE-599)KEP055120547 (OCoLC)857969078 (ZBM)1278.68021 (MITPRESS)6267536 (EBP)055120547 DE-627 ger DE-627 rda eng *68-02 msc 68T05 msc 62H30 msc Schapire, Robert E. verfasserin aut Boosting foundations and algorithms Robert E. Schapire and Yoav Freund Cambridge, Massachusetts MIT Press c2012 1 PDF (xv, 526 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 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. Mode of access: World Wide Web. Boosting (Algorithms) Supervised learning (Machine learning) Freund, Yoav mitwirkender ctb 9780262017183 Erscheint auch als Druck-Ausgabe 9780262017183 https://ieeexplore.ieee.org/book/6267536 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:1278.68021 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/1 22 01 0018 3848475081 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 3957240581 olr-MIT i z 23-07-21 100 01 3100 4472467690 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011221996 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740750375 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267536 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267536 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267536 100 01 3100 https://ieeexplore.ieee.org/book/6267536 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267536 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267536 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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| title_full |
Boosting foundations and algorithms Robert E. Schapire and Yoav Freund |
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Schapire, Robert E. |
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Boosting foundations and algorithms |
| abstract |
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. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 |
| abstractGer |
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. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 |
| abstract_unstemmed |
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. Includes bibliographical references and index. - Description based on PDF viewed 12/23/2015 |
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| title_short |
Boosting |
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