Advanced Structured Prediction - Couverture rigide

Couverture rigide

ISBN 10 : 0262028379 ISBN 13 : 9780262028370

Editeur : The MIT Press, 2014

Synopsis

An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs.

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.

Contributors
Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

A propos du livre

À propos de l?auteur

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, Tübingen, 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.

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, Tübingen, 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.

George Papandreou is a Research Scientist for Google, Inc.

Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.

Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.

Détails de l'édition

• Éditeur: The MIT Press
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 0262028379
• ISBN 13: 9780262028370
• Reliure: Relié
• Nombre de pages: 432
• Éditeur: Nowozin Sebastian, Gehler Peter V., Jancsary Jeremy, Lampert Christoph H.
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible