Advanced Structured Prediction (Neural Information Processing series)

Nowozin, Sebastian [Editor]; Gehler, Peter V. [Editor]; Jancsary, Jeremy [Editor]; Lampert, Christoph H. [Editor]; Nowozin, Sebastian [Contributor]; Gehler, Peter V. [Contributor]; Jancsary, Jeremy [Contributor]; Lampert, Christoph H. [Contributor]; ?ivný, Stanislav [Contributor]; Werner, Thomá? [Contributor]; Pru?a, Daniel [Contributor]; Martins, André F. T. [Contributor]; Schoenemann, Thomas [Contributor]; Kolmogorov, Vladimir [Contributor]; Meshi, Ofer [Contributor]; Jaakkola, Tommi [Contributor]; Globerson, Amir [Contributor]; Savchynskyy, Bogdan [Contributor]; Schmidt, Stefan [Contributor]; Papandreou, George [Contributor]; Yuille, Alan L. [Contributor]; Chen, Yutian [Contributor]; Gelfand, Andrew E. [Contributor]; Welling, Max [Contri

ISBN 10: 0262028379 / ISBN 13: 9780262028370

Published by The MIT Press, 2014

About this Item

Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

Seller Inventory # MIT-HC-VG-0262028379

Bibliographic Details

• Title: Advanced Structured Prediction (Neural Information Processing series)
• Publisher: The MIT Press
• Publication Date: 2014
• Language: English
• ISBN 10: 0262028379
• ISBN 13: 9780262028370
• Binding: hardcover
• Condition: Very Good
• Seller catalogs:
  • 1

About this title

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 Gigučre, 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 Průsa, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomás Werner, Alan Yuille, Stanislav Zivný

About the Authors

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.