1149 search results - page 21 / 230 » Learning Dynamic Bayesian Networks |
98
Voted
ICCV
2003
IEEE
15 years 2 months ago
2003
IEEE
This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion ...
118
click to vote
FLAIRS
2008
14 years 12 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: ...
TMI
2008
14 years 9 months ago
2008
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
78
Voted
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful...
NIPS
2000
14 years 11 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...