Hidden conditional random fields
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 no...
Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 29(2007), 10 vom: 15. Okt., Seite 1848-53 |
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1. Verfasser: | |
Weitere Verfasser: | , , , |
Format: | Aufsatz |
Sprache: | English |
Veröffentlicht: |
2007
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Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
Schlagworte: | Journal Article |
Zusammenfassung: | 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 |
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Beschreibung: | Date Completed 13.11.2007 Date Revised 16.08.2007 published: Print Citation Status MEDLINE |
ISSN: | 1939-3539 |