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We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function.
Statistical analysis of non-lattice data
Julian Besag · 1975
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
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An alternate objective function for markovian fields
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Shallow parsing with conditional random fields
Fei Sha and Fernando Pereira · 2003
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Discriminative fields for modeling spatial dependencies in natural images
Sanjiv Kumar and Martial Hebert · 2003
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Multiscale conditional random fields for image labeling
Xuming He, Richard S. Zemel, and Miguel Á. Carreira-Perpiñán · 2004
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Semi-markov conditional random fields for information extraction
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Integer linear programming inference for conditional random fields
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Hidden conditional random fields for phone classification
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2d conditional random fields for web information extraction
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Large margin methods for structured and interdependent output variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun · 2005
Training conditional random fields with multivariate evaluation measures
Jun Suzuki, Erik McDermott, and Hideki Isozaki · 2006
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Training conditional random fields for maximum labelwise accuracy
Samuel S. Gross, Olga Russakovsky, Chuong B. Do, and Serafim Batzoglou · 2007
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LETOR: Benchmark dataset for search on learning to rank for information retrieval
T. Liu, J. Xu, W. Xiong, and H. Li · 2007
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SoftRank: optimizing non-smooth rank metrics
Michael J. Taylor, John Guiver, Stephen Robertson, and Tom Minka · 2008
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BoltzRank: Learning to maximize expected ranking gain
Maksims Volkovs and Richard Zemel · 2009
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Piecewise Training for Undirected Models
C. Sutton and A. Mccallum · 2005
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Protein fold recognition using segmentation conditional random fields (scrfs)
Yan Liu, Jaime Carbonell, Peter Weigele, and Vanathi Gopalakrishnan · 2006
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J. Petterson, T. S. Caetano, J. J. McAuley, and J. Yu · 2009
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A primal-dual message-passing algorithm for approximated large scale structured prediction
Tamir Hazan and Raquel Urtasun · 2010
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Direct loss minimization for structured prediction
David McAllester, Tamir Hazan, and Joseph Keshet · 2010
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