Advanced structured prediction
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields includ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nowozin, Sebastian - 1980- [herausgeberIn] Gehler, Peter Vincent [herausgeberIn] Jancsary, Jeremy [herausgeberIn] Lampert, Christoph H. [herausgeberIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts London, England: The MIT Press ; 2014 © 2014 |
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| Schlagwörter: |
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| Anmerkung: |
Includes bibliographical references and index |
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| Umfang: |
1 Online-Ressource (ix, 415 pages) ; illustrations |
| Reihe: |
Neural information processing series |
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| Links: | |
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| ISBN: |
0-262-32295-1 978-0-262-32295-9 |
| Katalog-ID: |
863848672 |
|---|
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| 245 | 1 | 0 | |a Advanced structured prediction |c edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert |
| 264 | 1 | |a Cambridge, Massachusetts |a London, England |b The MIT Press |c [2014] | |
| 264 | 4 | |c © 2014 | |
| 300 | |a 1 Online-Ressource (ix, 415 pages) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
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| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Neural information processing series | |
| 500 | |a Includes bibliographical references and index | ||
| 505 | 8 | 0 | |t Introduction to structured prediction |r S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert |
| 505 | 8 | 0 | |t Training structure predictors through iterated logistic regression |r J. Domke |
| 505 | 8 | 0 | |t The power of LP relaxation for MAP inference |r S. Živný, T. Werner, and D. Průša |
| 505 | 8 | 0 | |t AD³, a fast decoder for structured prediction |r A. Martins |
| 505 | 8 | 0 | |t Generalized sequential tree-reweighted message passing |r T. Schoenemann and V. Kolmogorov |
| 505 | 8 | 0 | |t Smoother coordinate descent for MAP inference |r O. Meshi, T. Jaakkola, and A. Globerson |
| 505 | 8 | 0 | |t Getting feasible variable estimates from infeasible ones |r B. Savchynskyy and S. Schmidt |
| 505 | 8 | 0 | |t Perturb-and-MAP random fields |r G. Papandreou and A. Yuille |
| 505 | 8 | 0 | |t Herding for structure prediction |r Y. Chen, A.E. Gelfand, and M. Welling. |
| 505 | 8 | 0 | |t PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction |r S. Giguère, F. Laviolette, M. Marchand, and A. Rolland |
| 505 | 8 | 0 | |t Optimizing the measure of performance |r K. Keshet |
| 505 | 8 | 0 | |t Structured learning from cheap data |r X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht |
| 505 | 8 | 0 | |t Dynamic structure model selection |r D. Weiss and B. Taskar |
| 505 | 8 | 0 | |t Structured prediction from event detection |r M. Hoai and F. de la Terre |
| 505 | 8 | 0 | |t Structured prediction for object boundary detection in images |r S. Todorovic |
| 505 | 8 | 0 | |t Genome annotation with structure output learning |r J. Behr, G. Schweikert, and G. Rätsch. |
| 520 | |a The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. | ||
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| 650 | 4 | |a Data structures (Computer science) | |
| 650 | 4 | |a Epitaxial layers | |
| 650 | 4 | |a Excitons | |
| 650 | 4 | |a Nitrogen | |
| 650 | 4 | |a Radiative recombination | |
| 650 | 4 | |a Silicon carbide | |
| 650 | 4 | |a Temperature measurement | |
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0262322951 : electronic bk 0-262-32295-1 9780262322959 : electronic bk 978-0-262-32295-9 9780262028370 0262028379 (DE-627)863848672 (DE-576)9863848670 (DE-599)GBV863848672 (OCoLC)905493309 (MITPRESS)7008160 (EBP)05512271X DE-627 eng DE-627 rda eng XD-US Q325.5 006.3/1 23 Advanced structured prediction edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert Cambridge, Massachusetts London, England The MIT Press [2014] © 2014 1 Online-Ressource (ix, 415 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neural information processing series Includes bibliographical references and index Introduction to structured prediction S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert Training structure predictors through iterated logistic regression J. Domke The power of LP relaxation for MAP inference S. Živný, T. Werner, and D. Průša AD³, a fast decoder for structured prediction A. Martins Generalized sequential tree-reweighted message passing T. Schoenemann and V. Kolmogorov Smoother coordinate descent for MAP inference O. Meshi, T. Jaakkola, and A. Globerson Getting feasible variable estimates from infeasible ones B. Savchynskyy and S. Schmidt Perturb-and-MAP random fields G. Papandreou and A. Yuille Herding for structure prediction Y. Chen, A.E. Gelfand, and M. Welling. PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S. Giguère, F. Laviolette, M. Marchand, and A. Rolland Optimizing the measure of performance K. Keshet Structured learning from cheap data X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht Dynamic structure model selection D. Weiss and B. Taskar Structured prediction from event detection M. Hoai and F. de la Terre Structured prediction for object boundary detection in images S. Todorovic Genome annotation with structure output learning J. Behr, G. Schweikert, and G. Rätsch. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Machine learning Computer algorithms Data structures (Computer science) Machine learning Computer algorithms Data structures (Computer science) Epitaxial layers Excitons Nitrogen Radiative recombination Silicon carbide Temperature measurement Nowozin, Sebastian 1980- herausgeberin (DE-588)1075229596 (DE-627)833048562 (DE-576)443341532 edt Gehler, Peter Vincent herausgeberin (DE-588)142527572 (DE-627)636725073 (DE-576)330113224 edt Jancsary, Jeremy herausgeberin edt Lampert, Christoph H. herausgeberin edt 9780262028370 : hardcover Erscheint auch als Druck-Ausgabe Advanced structured prediction (DLC)2014013235 https://ieeexplore.ieee.org/book/7008160 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 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 3848471124 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1626962146 olr-MIT i z 26-07-16 62 01 0028 1655600141 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 30-12-16 100 01 3100 4472464446 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 4011217999 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 3740748400 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/7008160 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/7008160 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/7008160 62 01 0028 https://ieeexplore.ieee.org/book/7008160 100 01 3100 https://ieeexplore.ieee.org/book/7008160 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/7008160 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/7008160 2015 01 DE-93 https://ieeexplore.ieee.org/book/7008160 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2016.07.26 370 01 4370 2021.12.01 |
| spelling |
0262322951 : electronic bk 0-262-32295-1 9780262322959 : electronic bk 978-0-262-32295-9 9780262028370 0262028379 (DE-627)863848672 (DE-576)9863848670 (DE-599)GBV863848672 (OCoLC)905493309 (MITPRESS)7008160 (EBP)05512271X DE-627 eng DE-627 rda eng XD-US Q325.5 006.3/1 23 Advanced structured prediction edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert Cambridge, Massachusetts London, England The MIT Press [2014] © 2014 1 Online-Ressource (ix, 415 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neural information processing series Includes bibliographical references and index Introduction to structured prediction S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert Training structure predictors through iterated logistic regression J. Domke The power of LP relaxation for MAP inference S. Živný, T. Werner, and D. Průša AD³, a fast decoder for structured prediction A. Martins Generalized sequential tree-reweighted message passing T. Schoenemann and V. Kolmogorov Smoother coordinate descent for MAP inference O. Meshi, T. Jaakkola, and A. Globerson Getting feasible variable estimates from infeasible ones B. Savchynskyy and S. Schmidt Perturb-and-MAP random fields G. Papandreou and A. Yuille Herding for structure prediction Y. Chen, A.E. Gelfand, and M. Welling. PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S. Giguère, F. Laviolette, M. Marchand, and A. Rolland Optimizing the measure of performance K. Keshet Structured learning from cheap data X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht Dynamic structure model selection D. Weiss and B. Taskar Structured prediction from event detection M. Hoai and F. de la Terre Structured prediction for object boundary detection in images S. Todorovic Genome annotation with structure output learning J. Behr, G. Schweikert, and G. Rätsch. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Machine learning Computer algorithms Data structures (Computer science) Machine learning Computer algorithms Data structures (Computer science) Epitaxial layers Excitons Nitrogen Radiative recombination Silicon carbide Temperature measurement Nowozin, Sebastian 1980- herausgeberin (DE-588)1075229596 (DE-627)833048562 (DE-576)443341532 edt Gehler, Peter Vincent herausgeberin (DE-588)142527572 (DE-627)636725073 (DE-576)330113224 edt Jancsary, Jeremy herausgeberin edt Lampert, Christoph H. herausgeberin edt 9780262028370 : hardcover Erscheint auch als Druck-Ausgabe Advanced structured prediction (DLC)2014013235 https://ieeexplore.ieee.org/book/7008160 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 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 3848471124 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1626962146 olr-MIT i z 26-07-16 62 01 0028 1655600141 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 30-12-16 100 01 3100 4472464446 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 4011217999 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 3740748400 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/7008160 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/7008160 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/7008160 62 01 0028 https://ieeexplore.ieee.org/book/7008160 100 01 3100 https://ieeexplore.ieee.org/book/7008160 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/7008160 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/7008160 2015 01 DE-93 https://ieeexplore.ieee.org/book/7008160 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2016.07.26 370 01 4370 2021.12.01 |
| allfields_unstemmed |
0262322951 : electronic bk 0-262-32295-1 9780262322959 : electronic bk 978-0-262-32295-9 9780262028370 0262028379 (DE-627)863848672 (DE-576)9863848670 (DE-599)GBV863848672 (OCoLC)905493309 (MITPRESS)7008160 (EBP)05512271X DE-627 eng DE-627 rda eng XD-US Q325.5 006.3/1 23 Advanced structured prediction edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert Cambridge, Massachusetts London, England The MIT Press [2014] © 2014 1 Online-Ressource (ix, 415 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neural information processing series Includes bibliographical references and index Introduction to structured prediction S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert Training structure predictors through iterated logistic regression J. Domke The power of LP relaxation for MAP inference S. Živný, T. Werner, and D. Průša AD³, a fast decoder for structured prediction A. Martins Generalized sequential tree-reweighted message passing T. Schoenemann and V. Kolmogorov Smoother coordinate descent for MAP inference O. Meshi, T. Jaakkola, and A. Globerson Getting feasible variable estimates from infeasible ones B. Savchynskyy and S. Schmidt Perturb-and-MAP random fields G. Papandreou and A. Yuille Herding for structure prediction Y. Chen, A.E. Gelfand, and M. Welling. PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S. Giguère, F. Laviolette, M. Marchand, and A. Rolland Optimizing the measure of performance K. Keshet Structured learning from cheap data X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht Dynamic structure model selection D. Weiss and B. Taskar Structured prediction from event detection M. Hoai and F. de la Terre Structured prediction for object boundary detection in images S. Todorovic Genome annotation with structure output learning J. Behr, G. Schweikert, and G. Rätsch. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Machine learning Computer algorithms Data structures (Computer science) Machine learning Computer algorithms Data structures (Computer science) Epitaxial layers Excitons Nitrogen Radiative recombination Silicon carbide Temperature measurement Nowozin, Sebastian 1980- herausgeberin (DE-588)1075229596 (DE-627)833048562 (DE-576)443341532 edt Gehler, Peter Vincent herausgeberin (DE-588)142527572 (DE-627)636725073 (DE-576)330113224 edt Jancsary, Jeremy herausgeberin edt Lampert, Christoph H. herausgeberin edt 9780262028370 : hardcover Erscheint auch als Druck-Ausgabe Advanced structured prediction (DLC)2014013235 https://ieeexplore.ieee.org/book/7008160 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 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 3848471124 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1626962146 olr-MIT i z 26-07-16 62 01 0028 1655600141 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 30-12-16 100 01 3100 4472464446 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 4011217999 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 3740748400 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/7008160 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/7008160 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/7008160 62 01 0028 https://ieeexplore.ieee.org/book/7008160 100 01 3100 https://ieeexplore.ieee.org/book/7008160 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/7008160 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/7008160 2015 01 DE-93 https://ieeexplore.ieee.org/book/7008160 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2016.07.26 370 01 4370 2021.12.01 |
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0262322951 : electronic bk 0-262-32295-1 9780262322959 : electronic bk 978-0-262-32295-9 9780262028370 0262028379 (DE-627)863848672 (DE-576)9863848670 (DE-599)GBV863848672 (OCoLC)905493309 (MITPRESS)7008160 (EBP)05512271X DE-627 eng DE-627 rda eng XD-US Q325.5 006.3/1 23 Advanced structured prediction edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert Cambridge, Massachusetts London, England The MIT Press [2014] © 2014 1 Online-Ressource (ix, 415 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neural information processing series Includes bibliographical references and index Introduction to structured prediction S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert Training structure predictors through iterated logistic regression J. Domke The power of LP relaxation for MAP inference S. Živný, T. Werner, and D. Průša AD³, a fast decoder for structured prediction A. Martins Generalized sequential tree-reweighted message passing T. Schoenemann and V. Kolmogorov Smoother coordinate descent for MAP inference O. Meshi, T. Jaakkola, and A. Globerson Getting feasible variable estimates from infeasible ones B. Savchynskyy and S. Schmidt Perturb-and-MAP random fields G. Papandreou and A. Yuille Herding for structure prediction Y. Chen, A.E. Gelfand, and M. Welling. PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S. Giguère, F. Laviolette, M. Marchand, and A. Rolland Optimizing the measure of performance K. Keshet Structured learning from cheap data X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht Dynamic structure model selection D. Weiss and B. Taskar Structured prediction from event detection M. Hoai and F. de la Terre Structured prediction for object boundary detection in images S. Todorovic Genome annotation with structure output learning J. Behr, G. Schweikert, and G. Rätsch. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Machine learning Computer algorithms Data structures (Computer science) Machine learning Computer algorithms Data structures (Computer science) Epitaxial layers Excitons Nitrogen Radiative recombination Silicon carbide Temperature measurement Nowozin, Sebastian 1980- herausgeberin (DE-588)1075229596 (DE-627)833048562 (DE-576)443341532 edt Gehler, Peter Vincent herausgeberin (DE-588)142527572 (DE-627)636725073 (DE-576)330113224 edt Jancsary, Jeremy herausgeberin edt Lampert, Christoph H. herausgeberin edt 9780262028370 : hardcover Erscheint auch als Druck-Ausgabe Advanced structured prediction (DLC)2014013235 https://ieeexplore.ieee.org/book/7008160 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 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 3848471124 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1626962146 olr-MIT i z 26-07-16 62 01 0028 1655600141 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 30-12-16 100 01 3100 4472464446 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 4011217999 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 3740748400 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/7008160 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/7008160 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/7008160 62 01 0028 https://ieeexplore.ieee.org/book/7008160 100 01 3100 https://ieeexplore.ieee.org/book/7008160 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/7008160 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/7008160 2015 01 DE-93 https://ieeexplore.ieee.org/book/7008160 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 62 01 0028 OLR-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2016.07.26 370 01 4370 2021.12.01 |
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0262322951 : electronic bk 0-262-32295-1 9780262322959 : electronic bk 978-0-262-32295-9 9780262028370 0262028379 (DE-627)863848672 (DE-576)9863848670 (DE-599)GBV863848672 (OCoLC)905493309 (MITPRESS)7008160 (EBP)05512271X DE-627 eng DE-627 rda eng XD-US Q325.5 006.3/1 23 Advanced structured prediction edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert Cambridge, Massachusetts London, England The MIT Press [2014] © 2014 1 Online-Ressource (ix, 415 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neural information processing series Includes bibliographical references and index Introduction to structured prediction S. Nowozin, P.V. Gehler, J. Jancsary, and C.H. Lampert Training structure predictors through iterated logistic regression J. Domke The power of LP relaxation for MAP inference S. Živný, T. Werner, and D. Průša AD³, a fast decoder for structured prediction A. Martins Generalized sequential tree-reweighted message passing T. Schoenemann and V. Kolmogorov Smoother coordinate descent for MAP inference O. Meshi, T. Jaakkola, and A. Globerson Getting feasible variable estimates from infeasible ones B. Savchynskyy and S. Schmidt Perturb-and-MAP random fields G. Papandreou and A. Yuille Herding for structure prediction Y. Chen, A.E. Gelfand, and M. Welling. PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S. Giguère, F. Laviolette, M. Marchand, and A. Rolland Optimizing the measure of performance K. Keshet Structured learning from cheap data X. Lou, M. Kloft, G. Rätsch, and F.A. Hamprecht Dynamic structure model selection D. Weiss and B. Taskar Structured prediction from event detection M. Hoai and F. de la Terre Structured prediction for object boundary detection in images S. Todorovic Genome annotation with structure output learning J. Behr, G. Schweikert, and G. Rätsch. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Machine learning Computer algorithms Data structures (Computer science) Machine learning Computer algorithms Data structures (Computer science) Epitaxial layers Excitons Nitrogen Radiative recombination Silicon carbide Temperature measurement Nowozin, Sebastian 1980- herausgeberin (DE-588)1075229596 (DE-627)833048562 (DE-576)443341532 edt Gehler, Peter Vincent herausgeberin (DE-588)142527572 (DE-627)636725073 (DE-576)330113224 edt Jancsary, Jeremy herausgeberin edt Lampert, Christoph H. herausgeberin edt 9780262028370 : hardcover Erscheint auch als Druck-Ausgabe Advanced structured prediction (DLC)2014013235 https://ieeexplore.ieee.org/book/7008160 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext ZDB-37-IEM 2014 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_62 ISIL_DE-28 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 3848471124 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1626962146 olr-MIT i z 26-07-16 62 01 0028 1655600141 OLR-MIT Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 30-12-16 100 01 3100 4472464446 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 4011217999 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 3740748400 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/7008160 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/7008160 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/7008160 62 01 0028 https://ieeexplore.ieee.org/book/7008160 100 01 3100 https://ieeexplore.ieee.org/book/7008160 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/7008160 370 01 4370 E-Book: Zugriff im HCU-Netz. 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Introduction to structured prediction Training structure predictors through iterated logistic regression The power of LP relaxation for MAP inference AD³, a fast decoder for structured prediction Generalized sequential tree-reweighted message passing Smoother coordinate descent for MAP inference Getting feasible variable estimates from infeasible ones Perturb-and-MAP random fields Herding for structure prediction PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction Optimizing the measure of performance Structured learning from cheap data Dynamic structure model selection Structured prediction from event detection Structured prediction for object boundary detection in images Genome annotation with structure output learning |
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Introduction to structured prediction Training structure predictors through iterated logistic regression The power of LP relaxation for MAP inference AD³, a fast decoder for structured prediction Generalized sequential tree-reweighted message passing Smoother coordinate descent for MAP inference Getting feasible variable estimates from infeasible ones Perturb-and-MAP random fields Herding for structure prediction PAC-Bayesian risk bounds and learning algorithms for the regression approach to structured output prediction Optimizing the measure of performance Structured learning from cheap data Dynamic structure model selection Structured prediction from event detection Structured prediction for object boundary detection in images Genome annotation with structure output learning misc Q325.5 ddc 006.3/1 misc Machine learning misc Computer algorithms misc Data structures (Computer science) misc Epitaxial layers misc Excitons misc Nitrogen misc Radiative recombination misc Silicon carbide misc Temperature measurement Advanced structured prediction |
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The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Includes bibliographical references and index |
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The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Includes bibliographical references and index |
| abstract_unstemmed |
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, inc uding research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Includes bibliographical references and index |
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