The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
An Introduction to Conditional Random Fields
Open accessAbstract
Many tasks involve predicting a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling. They combine the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This survey describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in many areas, including natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large-scale CRFs. We do not assume previous knowledge of graphical modeling, so this survey is intended to be useful to practitioners in a wide variety of fields.
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Cited by
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
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Metadata
- Authors
- Sutton, C., & McCallum, A.
- Venue
- Foundations and Trends® in Machine Learning
- Publisher
- Emerald
- Published
- 2012-08-23
- Volume
- 4
- Issue
- 4
- Pages
- 267-373
- ISSN
- 1935-8237 (ISSN-L)
- 1935-8245
- OpenAlex
- W2129999749
- Cited by
- 564
- References
- 136
- FWCI
- 43.06
- Type
- journal article
- Language
- en
- Open access
- green
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