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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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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… 
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… 
Highly Cited
2007
Highly Cited
2007
1.1 Introduction Relational data has two characteristics: first, statistical dependencies exist between the entities we wish to… 
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… 
Highly Cited
2005
Highly Cited
2005
We present a Chinese word segmentation system submitted to the closed track of Sighan bakeoff 2005. Our segmenter was built using… 
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… 
Highly Cited
2004
Highly Cited
2004
This paper presents Japanese morphological analysis based on conditional random fields (CRFs). Previous work in CRFs assumed that… 
Highly Cited
2003
Highly Cited
2003
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at… 
Highly Cited
2003
Highly Cited
2003
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example… 
Highly Cited
2001
Highly Cited
2001
We present conditional random fields , a framework for building probabilistic models to segment and label sequence data…