Back Camp aleatori condicional Catalan Conditional Random Field German Campo aleatorio condicional Spanish میدان تصادفی شرطی Persian Champ aléatoire conditionnel French 条件付き確率場 Japanese 조건부 무작위장 Korean Умовне випадкове поле Ukrainian Trường điều kiện ngẫu nhiên Vietnamese 條件隨機域 Chinese
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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. To do so, the predictions are modelled as a graphical model, which represents the presence of 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, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions.
Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences,[1] part-of-speech tagging, shallow parsing,[2] named entity recognition,[3] gene finding, peptide critical functional region finding,[4] and object recognition[5] and image segmentation in computer vision.[6]
- ^ Lafferty, J.; McCallum, A.; Pereira, F. (2001). "Conditional random fields: Probabilistic models for segmenting and labeling sequence data". Proc. 18th International Conf. on Machine Learning. Morgan Kaufmann. pp. 282–289.
- ^ Sha, F.; Pereira, F. (2003). shallow parsing with conditional random fields.
- ^ Settles, B. (2004). "Biomedical named entity recognition using conditional random fields and rich feature sets" (PDF). Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications. pp. 104–107.
- ^ Chang KY; Lin T-p; Shih L-Y; Wang C-K (2015). "Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields". PLOS ONE. 10 (3): e0119490. Bibcode:2015PLoSO..1019490C. doi:10.1371/journal.pone.0119490. PMC 4372350. PMID 25803302.
- ^ J.R. Ruiz-Sarmiento; C. Galindo; J. Gonzalez-Jimenez (2015). "UPGMpp: a Software Library for Contextual Object Recognition.". 3rd. Workshop on Recognition and Action for Scene Understanding (REACTS).
- ^ He, X.; Zemel, R.S.; Carreira-Perpinñán, M.A. (2004). "Multiscale conditional random fields for image labeling". IEEE Computer Society. CiteSeerX 10.1.1.3.7826.
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