Everything about Conditional random field
Class of statistical modeling methods
In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction. Unlike a classifier which predicts a label for a single sample without considering neighboring samples, a CRF can take context into account. To do so, the predictions are modeled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph 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 neighbors. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Conditional random fieldA 5-minute orientation
- Skim a recent paper: Short-Term Wind Power Forecast Based on Continuous Conditional Random FieldInfluential work since 2022
- Pick up a classic book: A Christmas CarolThe most-reprinted book on the subject
- Read the landmark paper: Conditional random field reliability analysis of a cohesion-frictional slopeCited 148 times
- Go sideways: Structured predictionA closely related topic
- Go sideways: Hidden Markov modelA closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Conditional random field reliability analysis of a cohesion-frictional slope
Estimation of trend and random components of conditional random field using Gaussian process regression
Action categorization with modified hidden conditional random field
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
Component-Based License Plate Detection Using Conditional Random Field Model
Gastric histopathology image segmentation using a hierarchical conditional random field
Locating splicing forgery by fully convolutional networks and conditional random field
Gait-Based Gender Classification Using Mixed Conditional Random Field
🆕 Influential since 2022
Short-Term Wind Power Forecast Based on Continuous Conditional Random Field
Rockhead profile simulation using an improved generation method of conditional random field
Sematic segmentation of loess landslides with STAPLE mask and fully connected conditional random field
Iterative structure transformation and conditional random field based method for unsupervised multimodal change detection
Continuous conditional random field convolution for point cloud segmentation
📚 Books worth reading
A Christmas CarolCharles Dickens · 1843
Oliver TwistCharles Dickens · 1822
Great ExpectationsCharles Dickens · 1861
David CopperfieldCharles Dickens · 1800
The Pickwick PapersCharles Dickens · 1800
Little DorritCharles Dickens, Mary Sebag-Montefiore · 1800
Plays (36)William Shakespeare · 1623
The C Programming LanguageBrian W. Kernighan, Dennis MacAlistair Ritchie · 1978
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- Graphical model A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional…
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- Bayesian network A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a…
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