200 search results - page 6 / 40 » On the Consistency of Discrete Bayesian Learning |
NIPS
2007
13 years 9 months ago
2007
Clustering is often formulated as a discrete optimization problem. The objective is to find, among all partitions of the data set, the best one according to some quality measure....
ICML
2000
IEEE
14 years 3 days ago
2000
IEEE
The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining be...
NIPS
2000
13 years 9 months ago
2000
Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
JMLR
2000
13 years 7 months ago
2000
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
FLAIRS
2008
13 years 10 months ago
2008
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...