Conditional random fields (CRFs) are a class of statistical modeling methods 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 "neighbouring" samples, a CRF can take context into account.
CRFs find their applications in named entity recognition, part of speech tagging, gene prediction, noise reduction and object detection problems, to name a few.
Conditional Random Field is a special case of Markov Random field wherein the graph satisfies the property : “When we condition the graph on X globally i.e. when the values of random variables in X is fixed or given, all the random variables in set Y follow the Markov property p(Yᵤ/X,Yᵥ, u≠v) = p(Yᵤ/X,Yₓ, Yᵤ~Yₓ), where Yᵤ~Yₓ signifies that Yᵤ and Yₓ are neighbors in the graph.” A variable’s neighboring nodes or variables are also called the Markov Blanket of that variable.
https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776
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