Semisupervised learning of hidden Markov models via a homotopy method
Hidden Markov model (HMM) classifier design is considered for the analysis of sequential data, incorporating both labeled and unlabeled data for training; the balance between the use of labeled and unlabeled data is controlled by an allocation parameter \lambda \in [0, 1), where \lambda = 0 correspo...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 31(2009), 2 vom: 13. Feb., Seite 275-87 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2009
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Online verfügbar |
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