Conditional Random Fields (CRF)

Conditional random fields (CRFs) are statistical modeling methods used for structured prediction in pattern recognition and machine learning. Unlike classifiers, CRFs consider the context of neighboring samples by modeling predictions as a graphical model with dependencies between them. They are commonly used in natural language processing, image processing, and other applications such as part-of-speech tagging, named entity recognition, and object recognition.