-
2
-
-
0002051628
-
Empirical analysis of predictive algorithms for collaborative filtering
-
Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proc. UAI-98 (pp. 43-52).
-
(1998)
Proc. UAI-98
, pp. 43-52
-
-
Breese, J.S.1
Heckerman, D.2
Kadie, C.3
-
3
-
-
0002607026
-
Bayesian classification (AutoClass): Theory and results
-
Menlo Park, CA: AAAI Press
-
Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In Advances in knowledge discovery and data mining, 153-180. Menlo Park, CA: AAAI Press.
-
(1996)
Advances in Knowledge Discovery and Data Mining
, pp. 153-180
-
-
Cheeseman, P.1
Stutz, J.2
-
4
-
-
0041919126
-
-
(Technical Report MSR-TR-2002-103). Microsoft, Redmond, WA
-
Chickering, D. M. (2002). The WinMine Toolkit (Technical Report MSR-TR-2002-103). Microsoft, Redmond, WA.
-
(2002)
The WinMine Toolkit
-
-
Chickering, D.M.1
-
5
-
-
0002629270
-
Maximum likelihood from incomplete data via the em algorithm
-
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society B, 39, 1-38.
-
(1977)
J. Royal Statistical Society B
, vol.39
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
6
-
-
0031269184
-
On the optimality of the simple Bayesian classifier under zero-one loss
-
Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
-
(1997)
Machine Learning
, vol.29
, pp. 103-130
-
-
Domingos, P.1
Pazzani, M.2
-
7
-
-
0000854197
-
The Bayesian structural em algorithm
-
Friedman, N. (1998). The Bayesian structural EM algorithm. Proc. UAI-98 (pp. 129-138).
-
(1998)
Proc. UAI-98
, pp. 129-138
-
-
Friedman, N.1
-
8
-
-
0000319411
-
Learning Bayesian networks with local structure
-
Friedman, N., & Goldszmidt, M. (1996). Learning Bayesian networks with local structure. Proc. UAI-96 (pp. 252-262).
-
(1996)
Proc. UAI-96
, pp. 252-262
-
-
Friedman, N.1
Goldszmidt, M.2
-
9
-
-
0003860037
-
-
London, UK: Chapman and Hall
-
Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. London, UK: Chapman and Hall.
-
(1996)
Markov Chain Monte Carlo in Practice
-
-
Gilks, W.R.1
Richardson, S.2
Spiegelhalter, D.J.3
-
10
-
-
0002549585
-
Eigentaste: A constant time collaborative filtering algorithm
-
Goldberg, K., Roeder, T., Gupta, D., & Perkins, C. (2001). Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2), 133-151.
-
(2001)
Information Retrieval
, vol.4
, Issue.2
, pp. 133-151
-
-
Goldberg, K.1
Roeder, T.2
Gupta, D.3
Perkins, C.4
-
11
-
-
34249761849
-
Learning Bayesian networks: The combination of knowledge and statist, data
-
Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statist, data. Machine Learning, 20, 197-243.
-
(1995)
Machine Learning
, vol.20
, pp. 197-243
-
-
Heckerman, D.1
Geiger, D.2
Chickering, D.M.3
-
12
-
-
0001880210
-
KDD-Cup 2000 organizers' report: Peeling the onion
-
Kohavi, R., Brodley, C., Frasca, B., Mason, L., & Zheng, Z. (2000). KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explorations, 2, 86-98.
-
(2000)
SIGKDD Explorations
, vol.2
, pp. 86-98
-
-
Kohavi, R.1
Brodley, C.2
Frasca, B.3
Mason, L.4
Zheng, Z.5
-
15
-
-
0030120958
-
On the hardness of approximate reasoning
-
Roth, D. (1996). On the hardness of approximate reasoning. Artificial Intelligence, 82, 273-302.
-
(1996)
Artificial Intelligence
, vol.82
, pp. 273-302
-
-
Roth, D.1
-
16
-
-
84898975095
-
Generalized belief propagation
-
Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2001). Generalized belief propagation. In Adv. NIPS 13, 689-695.
-
(2001)
Adv. NIPS
, vol.13
, pp. 689-695
-
-
Yedidia, J.S.1
Freeman, W.T.2
Weiss, Y.3
|