444 search results - page 1 / 89 » Learning Nonlinear Dynamical Systems Using an EM Algorithm |
NIPS
1998
13 years 6 months ago
1998
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
ICANN
2009
Springer
13 years 9 months ago
2009
Springer
Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
NIPS
1998
13 years 6 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
PAMI
2008
13 years 4 months ago
2008
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...