-
2
-
-
0003846041
-
A tutorial on learning Bayesian networks
-
Microsoft Research, Tech Rep: MSR-TR-96-06
-
D Heckerman. A tutorial on learning Bayesian networks[R]. Microsoft Research, Tech Rep: MSR-TR-96-06, 1995
-
(1995)
-
-
Heckerman, D.1
-
3
-
-
34249832377
-
A Bayesian method for the induction of probabilistic networks from data
-
E Herskovits, G Cooper. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992, 9(4): 309-347
-
(1992)
Machine Learning
, vol.9
, Issue.4
, pp. 309-347
-
-
Herskovits, E.1
Cooper, G.2
-
4
-
-
0008580731
-
Learning Bayesian belief networks based on the minimum description length principle: Basic properties
-
J Suzuki. Learning Bayesian belief networks based on the minimum description length principle: Basic properties[J]. IEICE Trans on Fundamentals, 1999, E82(10): 2237-2245
-
(1999)
IEICE Trans on Fundamentals
, vol.E82
, Issue.10
, pp. 2237-2245
-
-
Suzuki, J.1
-
5
-
-
0033076357
-
Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
-
M L Wong, W Lam, K S Leung. Using evolutionary programming and minimum description length principle for data mining of Bayesian networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1999, 21(2): 174-178
-
(1999)
IEEE Trans on Pattern Analysis and Machine Intelligence
, vol.21
, Issue.2
, pp. 174-178
-
-
Wong, M.L.1
Lam, W.2
Leung, K.S.3
-
6
-
-
0034174383
-
A new approach for learning belief networks using independence criteria
-
M Luis, D Campos, J Huete. A new approach for learning belief networks using independence criteria[J]. International Journal of Approximate Reasoning, 2000, 24(1): 11-37
-
(2000)
International Journal of Approximate Reasoning
, vol.24
, Issue.1
, pp. 11-37
-
-
Luis, M.1
Campos, D.2
Huete, J.3
-
7
-
-
0036567524
-
Learning belief networks from data: An information theory based approach
-
J Cheng, R Greiner, J Kelly, et al. Learning belief networks from data: An information theory based approach[J]. Artificial Intelligence, 2002, 137(2): 43-90
-
(2002)
Artificial Intelligence
, vol.137
, Issue.2
, pp. 43-90
-
-
Cheng, J.1
Greiner, R.2
Kelly, J.3
-
8
-
-
0002219642
-
Learning Bayesian network structures from massive datasets: The sparse candidate algorithm
-
San Francisco, CA: Morgan Kanfmann
-
N Friedman, I Nachman, D Peer. Learning Bayesian network structures from massive datasets: The sparse candidate algorithm[C]. In: Proc of the 15th Conf on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kanfmann, 1999. 206-215
-
(1999)
Proc of the 15th Conf on Uncertainty in Artificial Intelligence
, pp. 206-215
-
-
Friedman, N.1
Nachman, I.2
Peer, D.3
-
9
-
-
4444358530
-
A hybrid approach to discover Bayesian networks from databases using evolutionary programming
-
Maebashi, Japan
-
M L Wong, S Y Lee, K-S Leung. A hybrid approach to discover Bayesian networks from databases using evolutionary programming[C]. Int'l Conf on Data Mining ICDM, Maebashi, Japan, 2002
-
(2002)
Int'l Conf on Data Mining ICDM
-
-
Wong, M.L.1
Lee, S.Y.2
Leung, K.-S.3
-
10
-
-
15544385739
-
An improved Bayesian networks learning algorithm
-
Chinese source
-
Qiang Lei, Xiao Tianyuan, Qiao Guixiu. An improved Bayesian networks learning algorithm[J]. Journal of Computer Research and Development, 2002, 39(10): 1221-1226 (in Chinese)
-
(2002)
Journal of Computer Research and Development
, vol.39
, Issue.10
, pp. 1221-1226
-
-
Qiang, L.1
Xiao, T.2
Qiao, G.3
-
11
-
-
0033335693
-
Learning Bayesian belief networks based on the minimum description length principle: An efficient algorithm using the B and B technique
-
J Suzuki. Learning Bayesian belief networks based on the minimum description length principle: An efficient algorithm using the B and B technique[J]. IEICE Trans on Information and Systems, 1999, E82-D(2): 356-367
-
(1999)
IEICE Trans on Information and Systems
, vol.E82-D
, Issue.2
, pp. 356-367
-
-
Suzuki, J.1
|