Advanced Structured Prediction Contributor(s): Nowozin, Sebastian (Editor), Gehler, Peter V. (Editor), Jancsary, Jeremy (Editor) |
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ISBN: 0262028379 ISBN-13: 9780262028370 Publisher: MIT Press OUR PRICE: $61.75 Product Type: Hardcover - Other Formats Published: December 2014 |
Additional Information |
BISAC Categories: - Computers | Machine Theory - Computers | Computer Vision & Pattern Recognition |
Dewey: 006.31 |
LCCN: 2014013235 |
Series: Neural Information Processing |
Physical Information: 1" H x 8.3" W x 10.3" (2.30 lbs) 432 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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 |
Contributor Bio(s): Lampert, Christoph H.: - 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.Nowozin, Sebastian: - Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.Jancsary, Jeremy: - Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna.Gehler, Peter V.: - Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, Tübingen, Germany.Nowozin, Sebastian: - Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England.Gehler, Peter V.: - Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, Tübingen, Germany.Jancsary, Jeremy: - Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna.Papandreou, George: - George Papandreou is a Research Scientist for Google, Inc.Yuille, Alan L.: - Alan Yuille is Professor in the Department of Statistics, University of California, Los Angeles.Taskar, Ben: - Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.Dietterich, Thomas G.: - Thomas G. Dietterich is Professor of Computer Science at Oregon State University.Lampert, Christoph H.: - 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.Jordan, Michael I.: - Michael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley, and recipient of the ACM/AAAI Allen Newell Award. |