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Conditional random field
Known as:
CRF
, Conditional random fields
, Discriminative probabilistic latent variable model
Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they…
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.NET Framework
Activity recognition
Bioinformatics
C++
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Broader (1)
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2015
Highly Cited
2015
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng
,
Sadeep Jayasumana
,
+5 authors
Philip H. S. Torr
IEEE International Conference on Computer Vision…
2015
Corpus ID: 1318262
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have…
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Review
2012
Review
2012
An Introduction to Conditional Random Fields
Charles Sutton
,
A. McCallum
Found. Trends Mach. Learn.
2012
Corpus ID: 342976
Many tasks involve predicting a large number of variables that depend on each other as well as on other observed variables…
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Highly Cited
2007
Highly Cited
2007
Learning Conditional Random Fields for Stereo
Daniel Scharstein
,
C. Pal
IEEE Conference on Computer Vision and Pattern…
2007
Corpus ID: 13154466
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose…
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Highly Cited
2007
Highly Cited
2007
An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton
,
A. McCallum
2007
Corpus ID: 12943282
1.1 Introduction Relational data has two characteristics: first, statistical dependencies exist between the entities we wish to…
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Highly Cited
2006
Highly Cited
2006
Hidden Conditional Random Fields for Gesture Recognition
Sy Bor Wang
,
A. Quattoni
,
Louis-Philippe Morency
,
D. Demirdjian
,
Trevor Darrell
IEEE Computer Society Conference on Computer…
2006
Corpus ID: 1171329
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex…
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Highly Cited
2004
Highly Cited
2004
Multiscale conditional random fields for image labeling
Xuming He
,
R. Zemel
,
M. A. Carreira-Perpiñán
Proceedings of the IEEE Computer Society…
2004
Corpus ID: 11859305
We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set…
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Highly Cited
2004
Highly Cited
2004
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
Charles Sutton
,
A. McCallum
,
Khashayar Rohanimanesh
J. Mach. Learn. Res.
2004
Corpus ID: 6038991
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded…
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Highly Cited
2003
Highly Cited
2003
Shallow Parsing with Conditional Random Fields
Fei Sha
,
Fernando C Pereira
NAACL
2003
Corpus ID: 13936575
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at…
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Highly Cited
2003
Highly Cited
2003
Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
A. McCallum
,
Wei Li
CoNLL
2003
Corpus ID: 11664683
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example…
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Highly Cited
2001
Highly Cited
2001
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
J. Lafferty
,
A. McCallum
,
Fernando Pereira
ICML
2001
Corpus ID: 219683473
We present conditional random fields , a framework for building probabilistic models to segment and label sequence data…
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