80 search results - page 1 / 16 » Learning non-stationary conditional probability distribution... |
CORR
2008
Springer
13 years 5 months ago
2008
Springer
Information estimates such as the "direct method" of Strong et al. (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by...
NN
2000
Springer
13 years 4 months ago
2000
Springer
While sophisticated neural networks and graphical models have been developed for predicting conditional probabilities in a non-stationary environment, major improvements in the tr...
FLAIRS
2006
13 years 6 months ago
2006
A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
AAAI
2011
12 years 4 months ago
2011
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
ECML
2004
Springer
13 years 10 months ago
2004
Springer
It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probabilit...