Advanced structured prediction / edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert.
Contributor(s): Nowozin, Sebastian [editor.]
| IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Neural information processing series: Publisher: Cambridge, Massachusetts : MIT Press, [2014]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2014]Description: 1 PDF (ix, 415 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262322959.Subject(s): Machine learning | Computer algorithms | Data structures (Computer science) | Epitaxial layers | Excitons | Nitrogen | Radiative recombination | Silicon carbide | Temperature measurementGenre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 006.3/1 Online resources: Abstract with links to resource Also available in print.Summary: 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, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England. Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, T�ubingen, Germany. Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna. Christoph H. Lampert is Assistant Professor at the Institute of Science and Technology Austria, where he heads a group for Computer Vision and Machine Learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, S�ebastien Gigu�re, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, Fran�ois Laviolette, Xinghua Lou, Mario Marchand, Andr�e F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Pru - �a, Gunnar R��tsch, Am�elie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thom� - � Werner, Alan Yuille, Stanislav - �ivn��.
Includes bibliographical references and index.
Restricted to subscribers or individual electronic text purchasers.
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, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England. Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, T�ubingen, Germany. Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna. Christoph H. Lampert is Assistant Professor at the Institute of Science and Technology Austria, where he heads a group for Computer Vision and Machine Learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, S�ebastien Gigu�re, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, Fran�ois Laviolette, Xinghua Lou, Mario Marchand, Andr�e F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Pru - �a, Gunnar R��tsch, Am�elie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thom� - � Werner, Alan Yuille, Stanislav - �ivn��.
Also available in print.
Mode of access: World Wide Web
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