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| ==en construction==
| | #REDIRECTION[[Champ aléatoire conditionnel]] |
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| == Définition ==
| | [[Catégorie:ENGLISH]] |
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| == Français ==
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| ''' XXXXXXXXX '''
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| == Anglais ==
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| ''' Conditional random field '''
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| Conditional random fields (CRFs) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighboring" samples, a CRF can take context into account. To do so, the prediction is modeled as a graphical model, which implements dependencies between the predictions. What kind of graph is used depends on the application. For example, in natural language processing, linear chain CRFs are popular, which implement sequential dependencies in the predictions. In image processing the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions.
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Version du 9 décembre 2021 à 11:43
