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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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.NET FrameworkActivity recognitionBioinformaticsC++
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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
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
Learning Conditional Random Fields for Stereo
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
An Introduction to Conditional Random Fields for Relational Learning
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
Document Summarization Using Conditional Random Fields
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
Hidden Conditional Random Fields for Gesture Recognition
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
Multiscale conditional random fields for image labeling
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
Semi-Markov Conditional Random Fields for Information Extraction
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
Applying Conditional Random Fields to Japanese Morphological Analysis
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
Shallow Parsing with Conditional Random Fields
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
Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example… Expand
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