Abstract

We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.

Keywords

Discriminative modelConditional random fieldLatent variableArtificial intelligenceHidden variable theoryComputer sciencePattern recognition (psychology)Random variableRandom fieldGraphMachine learningMathematicsTheoretical computer scienceStatistics

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Publication Info

Year
2007
Type
article
Volume
29
Issue
10
Pages
1848-1852
Citations
489
Access
Closed

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Cite This

Ariadna Quattoni, Sybor Wang, Louis‐Philippe Morency et al. (2007). Hidden Conditional Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence , 29 (10) , 1848-1852. https://doi.org/10.1109/tpami.2007.1124

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DOI
10.1109/tpami.2007.1124