Hidden Conditional Random Fields | RDL Research Database

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

Affiliated Institutions

Massachusetts Institute of Technology US

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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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APA Style

                            
                                
                                    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
                        

Identifiers

DOI: 10.1109/tpami.2007.1124