40 search results - page 4 / 8 » Monte Carlo Hidden Markov Models: Learning Non-Parametric Mo... |
ECCV
2002
Springer
14 years 7 months ago
2002
Springer
Abstract. Natural scenes consist of a wide variety of stochastic patterns. While many patterns are represented well by statistical models in two dimensional regions as most image s...
NIPS
2001
13 years 6 months ago
2001
This paper proposes an approach to classification of adjacent segments of a time series as being either of classes. We use a hierarchical model that consists of a feature extract...
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
JMLR
2010
13 years 3 days ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
CVPR
2009
IEEE
15 years 7 days ago
2009
IEEE
A Bayesian marked point process (MPP) model is developed
to detect and count people in crowded scenes. The
model couples a spatial stochastic process governing number
and placem...