5 search results - page 1 / 1 » Approximating the Covariance Matrix of GMMs with Low-Rank Pe... |
IDEAL
2010
Springer
13 years 4 months ago
2010
Springer
: Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d2 elements of the covariance (in d dimensions) is costly and could result ...
SIAMSC
2011
13 years 2 months ago
2011
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
TSP
2008
13 years 7 months ago
2008
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...
ILP
2003
Springer
14 years 19 days ago
2003
Springer
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
ECCV
2002
Springer
14 years 8 months ago
2002
Springer
The context of this work is lateral vehicle control using a camera as a sensor. A natural tool for controlling a vehicle is recursive filtering. The well-known Kalman fil...