-
1
-
-
9444228194
-
Hierarchical hidden Markov models with general state hierarchy
-
San Jose, CA, Jul
-
H. H. Bui, D. Q. Phung, and S. Venkatesh. Hierarchical hidden Markov models with general state hierarchy. In AAAI, pages 324-329, San Jose, CA, Jul 2004.
-
(2004)
AAAI
, pp. 324-329
-
-
Bui, H.H.1
Phung, D.Q.2
Venkatesh, S.3
-
2
-
-
0032119668
-
The hierarchical hidden Markov model: Analysis and applications
-
S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden Markov model: Analysis and applications. Machine Learning, 32(1):41-62, 1998.
-
(1998)
Machine Learning
, vol.32
, Issue.1
, pp. 41-62
-
-
Fine, S.1
Singer, Y.2
Tishby, N.3
-
3
-
-
9444286980
-
Interactive information extraction with constrained conditional random fields
-
San Jose, CA
-
T. Kristjannson, A. Culotta, P. Viola, and A. McCallum. Interactive information extraction with constrained conditional random fields. In AAAI, pages 412-418, San Jose, CA, 2004.
-
(2004)
AAAI
, pp. 412-418
-
-
Kristjannson, T.1
Culotta, A.2
Viola, P.3
McCallum, A.4
-
4
-
-
0142192295
-
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
-
J. Lafferty, A.McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282-289, 2001.
-
(2001)
ICML
, pp. 282-289
-
-
Lafferty, J.1
McCallum, A.2
Pereira, F.3
-
6
-
-
0013288412
-
-
PhD thesis, Computer Science Division, University of California, Berkeley, Jul
-
K. Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, Computer Science Division, University of California, Berkeley, Jul 2002.
-
(2002)
Dynamic Bayesian Networks: Representation, Inference and Learning
-
-
Murphy, K.1
-
7
-
-
24644492941
-
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models
-
Jun
-
N. Nguyen, D. Phung, S. Venkatesh, and H. H. Bui. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models. In CVPR, volume 2, pages 955-960, Jun 2005.
-
(2005)
CVPR
, vol.2
, pp. 955-960
-
-
Nguyen, N.1
Phung, D.2
Venkatesh, S.3
Bui, H.H.4
-
8
-
-
34047192804
-
Semi-Markov conditional random fields for information extraction
-
S. Sarawagi and W. W. Cohen. Semi-Markov conditional random fields for information extraction. In NIPS. 2004.
-
(2004)
NIPS
-
-
Sarawagi, S.1
Cohen, W.W.2
-
10
-
-
33947615175
-
Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data
-
C. Sutton, A. McCallum, and K. Rohanimanesh. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. JMLR, 8:693-723, Mar 2007. (Pubitemid 46491655)
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 693-723
-
-
Sutton, C.1
McCallum, A.2
Rohanimanesh, K.3
-
11
-
-
84858769183
-
Hierarchical semi-Markov conditional random fields for recursive sequential data
-
T. T. Truyen, D. Q. Phung, H. H. Bui, and S. Venkatesh. Hierarchical semi-Markov conditional random fields for recursive sequential data. Technical report, Curtin University of Technology, http://www.computing.edu.au/~trantt2/ pubs/hcrf.pdf, 2008.
-
(2008)
Technical Report, Curtin University of Technology
-
-
Truyen, T.T.1
Phung, D.Q.2
Bui, H.H.3
Venkatesh, S.4
-
12
-
-
58349114205
-
Scene segmentation with CRFs learned from partially labeled images
-
MIT Press
-
J. Verbeek and B. Triggs. Scene segmentation with CRFs learned from partially labeled images. In Advances in Neural Information Processing Systems 20, pages 1553-1560. MIT Press, 2008.
-
(2008)
Advances in Neural Information Processing Systems
, vol.20
, pp. 1553-1560
-
-
Verbeek, J.1
Triggs, B.2
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