-
2
-
-
0003507803
-
-
Prentice-Hall Inc.
-
P. Cabena, P. Hadjinian, R. Stadter, J. Verhees, and A. Zansi. Discovering Data Mining: From Concept to Implementation. Prentice-Hall Inc., 1997.
-
(1997)
Discovering Data Mining: From Concept to Implementation
-
-
Cabena, P.1
Hadjinian, P.2
Stadter, R.3
Verhees, J.4
Zansi, A.5
-
3
-
-
0036567524
-
Learning Bayesian network from data: An information-theory based approached
-
J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu. Learning Bayesian network from data: An information-theory based approached. Artificial Intelligence, 137:43-90, 2002.
-
(2002)
Artificial Intelligence
, vol.137
, pp. 43-90
-
-
Cheng, J.1
Greiner, R.2
Kelly, J.3
Bell, D.4
Liu, W.5
-
6
-
-
0003846041
-
-
Technical report, Microsoft Research, Advanced Technology Division, March
-
D. Heckerman. A tutorial on learning Bayesian networks. Technical report, Microsoft Research, Advanced Technology Division, March 1995.
-
(1995)
A Tutorial on Learning Bayesian Networks
-
-
Heckerman, D.1
-
7
-
-
34249832377
-
A Bayesian method for the induction of probabilistic networks from data
-
E. Herskovits and G. Cooper. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309-347, 1992.
-
(1992)
Machine Learning
, vol.9
, Issue.4
, pp. 309-347
-
-
Herskovits, E.1
Cooper, G.2
-
9
-
-
0002610991
-
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches
-
D. Heckerman and J. Whittaker, editors, Fort Lauderdale, Florida, January Morgan Kaufmann
-
E. J. Keogh and M. J. Pazzani. Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. In D. Heckerman and J. Whittaker, editors, Proceedings of the Seventh International Workshop on A1 and Statistics, pages 225-230, Fort Lauderdale, Florida, January 1999. Morgan Kaufmann.
-
(1999)
Proceedings of the Seventh International Workshop on A1 and Statistics
, pp. 225-230
-
-
Keogh, E.J.1
Pazzani, M.J.2
-
10
-
-
0028482006
-
Learning Bayesian belief networks-an approach based on the MDL principle
-
W. Lam and F. Bacchus. Learning Bayesian belief networks-an approach based on the MDL principle. Computational Intelligence, 10(4):269-293, 1994.
-
(1994)
Computational Intelligence
, vol.10
, Issue.4
, pp. 269-293
-
-
Lam, W.1
Bacchus, F.2
-
11
-
-
0001901666
-
Induction of selective Bayesian classifier
-
R. L. de Mantaras and D. Poole, editors, Morgan Kaufmann
-
P. Langley and S. Sage. Induction of selective Bayesian classifier. In R. L. de Mantaras and D. Poole, editors, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, July 1994. Morgan Kaufmann.
-
Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, July 1994
-
-
Langley, P.1
Sage, S.2
-
12
-
-
0030245966
-
Structural learning of Bayesian network by genetic algorithms: A performance analysis of control parameters
-
September
-
P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, and C. Kuijpers. Structural learning of Bayesian network by genetic algorithms: A performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9):912-926, September 1996.
-
(1996)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.18
, Issue.9
, pp. 912-926
-
-
Larrañaga, P.1
Poza, M.2
Yurramendi, Y.3
Murga, R.4
Kuijpers, C.5
-
14
-
-
0007118287
-
Database marketing: Past present, and future
-
L. A. Petrison, R. C. Blattberg, and P. Wang. Database marketing: Past present, and future. Journal of Direct Marketing, 11(4):109-125, 1997.
-
(1997)
Journal of Direct Marketing
, vol.11
, Issue.4
, pp. 109-125
-
-
Petrison, L.A.1
Blattberg, R.C.2
Wang, P.3
-
17
-
-
0003614273
-
-
MIT Press, MA, second edition
-
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, MA, second edition, 2000.
-
(2000)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
18
-
-
0033076357
-
Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
-
February
-
M. L. Wong, W. Lam, and K. S. Leung. Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(2):174-178, February 1999.
-
(1999)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.21
, Issue.2
, pp. 174-178
-
-
Wong, M.L.1
Lam, W.2
Leung, K.S.3
-
19
-
-
0002082928
-
Issues and problems in applying neural computing to target marketing
-
J. Zahavi and N. Levin. Issues and problems in applying neural computing to target marketing. Journal of Direct Marketing, 11(4):63-75, 1997.
-
(1997)
Journal of Direct Marketing
, vol.11
, Issue.4
, pp. 63-75
-
-
Zahavi, J.1
Levin, N.2
|