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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… Expand
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Papers overview

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Highly Cited
2015
Highly Cited
2015
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have… Expand
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Highly Cited
2007
Highly Cited
2007
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose… Expand
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Highly Cited
2007
Highly Cited
2007
1.1 Introduction Relational data has two characteristics: first, statistical dependencies exist between the entities we wish to… Expand
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Highly Cited
2007
Highly Cited
2007
Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most… Expand
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Highly Cited
2006
Highly Cited
2006
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex… Expand
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Highly Cited
2004
Highly Cited
2004
We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set… Expand
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Highly Cited
2004
Highly Cited
2004
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively… Expand
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Highly Cited
2004
Highly Cited
2004
This paper presents Japanese morphological analysis based on conditional random fields (CRFs). Previous work in CRFs assumed that… Expand
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Highly Cited
2003
Highly Cited
2003
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at… Expand
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Highly Cited
2003
Highly Cited
2003
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example… Expand
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