David Heckerman

See: Bayesian Network Learning Algorithm, Colloborative Filtering Algorithm.

References

(Meila & Heckerman, 2001) ⇒ Marina Meila, David Heckerman. (2001). “An Experimental Comparison of Model-based Clustering Methods.” In: Machine Learning, 42(1/2). doi:10.1023/A:1007648401407

(Breese et al., 1998) ⇒ John S. Breese, David Heckerman, and Carl Kadie. (1998). “Empirical Analysis of Predictive Algorithms for Collaborative Filtering." In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998).
(Dumais et al., 1998) ⇒ Susan T. Dumais, John C. Platt, David Heckerman, and Mehran Sahami. (1998). “Inductive Learning Algorithms and Representations for Text Categorization.” In: Proceedings of the Seventh International Conference on Information and Knowledge Management (CIKM 1998).
(Sahami et al., 1998) ⇒ Mehran Sahami, Susan T. Dumais, David Heckerman, & Eric Horvitz. (1998). A Bayesian Approach to Filtering Junk E-mail. AAAI-98 Workshop on Learning for Text Categorization. Tech. Rep. WS-98-05, AAAI Press. http://robotics. Stanford.edu/users/sahami/papers.html.
(Horvitz et al., 1998) ⇒ Eric Horvitz, Jack Breese, David Heckerman, David Hovel, and Koos Rommelse. (1998). “The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users.” In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence.

(Chickeing & Heckerman, 1997) ⇒ David M. Chickering, and David Heckerman. (1997). “Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables.” In: Machine Learning, 29.

(Heckerman, 1996a) ⇒ David Heckerman. (1996). “A Tutorial on Learning with Bayesian Networks.” Technical Report MSR-TR-95-06, Microsoft Corporation.
(Heckerman, 1996b) ⇒ David Heckerman. (1996). “Bayesian Networks for Knowledge Discovery.” In: (Fayyad et al., 1996a)

(Heckerman et al., 1995) ⇒ David Heckerman, Dan Geiger, and David M. Chickering. (1995). “Learning Bayesian networks: The combination of knowledge and statistical data.” In: Machine Learning, 20(3). doi:10.1007/BF00994016