CRF

2000년 도입 · 논문 468편에서 사용

Conditional Random Fields or CRFs are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions. Image Credit: Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields