53 search results - page 7 / 11 » Learning first-order Markov models for control |
85
Voted
NN
1997
Springer
15 years 1 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
56
Voted
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
IPMI
2009
Springer
15 years 2 months ago
2009
Springer
Abstract. In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method ...
ICMLA
2004
14 years 11 months ago
2004
Predictive state representation (PSR) models for controlled dynamical systems have recently been proposed as an alternative to traditional models such as partially observable Mark...