Learning with Probabilistic Representations (Machine Learning Special Issue)
Tom Dietterich
tgd at CS.ORST.EDU
Tue Dec 30 20:09:13 EST 1997
Machine Learning has published the following special issue on
LEARNING WITH PROBABILISTIC REPRESENTATIONS
Guest Editors: Pat Langley, Gregory M. Provan, and Padhraic Smyth
Learning with Probabilistic Representations
Pat Langley, Gregory Provan, and Padhraic Smyth
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Pedro Domingos and Michael Pazzani
Bayesian Network Classifiers
Nir Friedman, Dan Geiger, and Moises Goldszmidt
The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Sanjoy Dasgupta
Efficient Approximations for the Marginal Likelihood of Bayesian
Networks with Hidden Variables
David Maxwell Chickering and David Heckerman
Adaptive Probabilistic Networks with Hidden Variables
John Binder, Daphne Koller, Stuart Russell, and Keiji Kanazawa
Factorial Hidden Markov Models
Zoubin Ghahramani and Michael I. Jordan
Predicting Protein Secondary Structure using Stochastic Tree Grammars
Naoki Abe and Hiroshi Mamitsuka
For more information, see http://www.cs.orst.edu/~tgd/mlj
--Tom
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