-
1
-
-
2442627902
-
Hierarchical bayesian models for applications in information retrieval
-
J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, M. West, et al. Hierarchical bayesian models for applications in information retrieval. In Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting, page 25, 2003.
-
(2003)
Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting
, pp. 25
-
-
Bernardo, J.1
Bayarri, M.2
Berger, J.3
Dawid, A.4
Heckerman, D.5
Smith, A.6
West, M.7
-
3
-
-
80051762104
-
Distributed optimization and statistical learning via the alternating direction method of multipliers
-
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends R in Machine Learning, 3(1):1{122, 2011.
-
(2011)
Foundations and Trends R in Machine Learning
, vol.3
, Issue.1
, pp. 1-122
-
-
Boyd, S.1
Parikh, N.2
Chu, E.3
Peleato, B.4
Eckstein, J.5
-
4
-
-
0042496103
-
Learning equivalence classes of bayesian-network structures
-
D. Chickering. Learning equivalence classes of bayesian-network structures. The Journal of Machine Learning Research, 2:445{498, 2002.
-
(2002)
The Journal of Machine Learning Research
, vol.2
, pp. 445-498
-
-
Chickering, D.1
-
5
-
-
15944399178
-
Sparse graphical models for exploring gene expression data
-
A. Dobra, C. Hans, B. Jones, J. Nevins, G. Yao, and M. West. Sparse graphical models for exploring gene expression data. Journal of Multivariate Analysis, 90(1):196{212, 2004.
-
(2004)
Journal of Multivariate Analysis
, vol.90
, Issue.1
, pp. 196-212
-
-
Dobra, A.1
Hans, C.2
Jones, B.3
Nevins, J.4
Yao, G.5
West, M.6
-
6
-
-
70450265503
-
Optimized cutting plane algorithm for large-scale risk minimization
-
V. Franc and S. Sonnenburg. Optimized cutting plane algorithm for large-scale risk minimization. The Journal of Machine Learning Research, 10:2157{2192, 2009.
-
(2009)
The Journal of Machine Learning Research
, vol.10
, pp. 2157-2192
-
-
Franc, V.1
Sonnenburg, S.2
-
7
-
-
45849134070
-
Sparse inverse covariance estimation with the graphical lasso
-
J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432{441, 2008.
-
(2008)
Biostatistics
, vol.9
, Issue.3
, pp. 432-441
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
8
-
-
0002219642
-
Learning bayesian network structure from massive datasets: The Òsparse candidateÓ algorithm background: Learning structure
-
N. Friedman. Learning bayesian network structure from massive datasets: The Òsparse candidateÓ algorithm background: Learning structure. Science, pages 206{215, 1999.
-
(1999)
Science
, pp. 206-215
-
-
Friedman, N.1
-
10
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1):389{422, 2002.
-
(2002)
Machine Learning
, vol.46
, Issue.1
, pp. 389-422
-
-
Guyon, I.1
Weston, J.2
Barnhill, S.3
Vapnik, V.4
-
11
-
-
84856268146
-
-
Lawrence Berkeley National Laboratory
-
T. Hong, F. Buhl, P. Haves, S. Selkowitz, and M. Wetter. Comparing computer run time of building simulation programs. Lawrence Berkeley National Laboratory, 2008.
-
(2008)
Comparing Computer Run Time of Building Simulation Programs
-
-
Hong, T.1
Buhl, F.2
Haves, P.3
Selkowitz, S.4
Wetter, M.5
-
13
-
-
0033225865
-
An introduction to variational methods for graphical models
-
M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. Machine learning, 37(2):183{233, 1999.
-
(1999)
Machine Learning
, vol.37
, Issue.2
, pp. 183-233
-
-
Jordan, M.1
Ghahramani, Z.2
Jaakkola, T.3
Saul, L.4
-
15
-
-
0030245966
-
Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters
-
P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, and C. Kuijpers. Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 18(9):912{926, 1996.
-
(1996)
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol.18
, Issue.9
, pp. 912-926
-
-
Larrañaga, P.1
Poza, M.2
Yurramendi, Y.3
Murga, R.4
Kuijpers, C.5
-
22
-
-
0000120766
-
Estimating the dimension of a model
-
G. Schwarz. Estimating the dimension of a model. The annals of statistics, 6(2):461{464, 1978.
-
(1978)
The Annals of Statistics
, vol.6
, Issue.2
, pp. 461-464
-
-
Schwarz, G.1
-
24
-
-
56449094547
-
A scalable modular convex solver for regularized risk minimization
-
ACM
-
C. H. Teo, Q. Le, A. Smola, and S. V. N. Vishwanathan. A scalable modular convex solver for regularized risk minimization. In In KDD. ACM, 2007.
-
(2007)
KDD
-
-
Teo, C.H.1
Le, Q.2
Smola, A.3
Vishwanathan, S.V.N.4
-
26
-
-
33746035971
-
The max-min hill-climbing bayesian network structure learning algorithm
-
I. Tsamardinos, L. Brown, and C. Aliferis. The max-min hill-climbing bayesian network structure learning algorithm. Machine learning, 65(1):31{78, 2006.
-
(2006)
Machine Learning
, vol.65
, Issue.1
, pp. 31-78
-
-
Tsamardinos, I.1
Brown, L.2
Aliferis, C.3
|