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» Dynamic Modeling in Inductive Inference
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CVIU
2007
193views more  CVIU 2007»
13 years 8 months ago
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
This paper addresses the problem of object detection and recognition in complex scenes, where objects are partially occluded. The approach presented herein is based on the hypothe...
Sinisa Todorovic, Michael C. Nechyba
ICPR
2006
IEEE
14 years 9 months ago
Multi-modal Sequential Monte Carlo for On-Line Hierarchical Graph Structure Estimation in Model-based Scene Interpretation
We present a computationally efficient, on-line graph structure estimation method for model-based scene interpretation. Different scenes have different hierarchical graphical mode...
In-So Kweon, Sungho Kim
SDM
2009
SIAM
208views Data Mining» more  SDM 2009»
14 years 5 months ago
A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.
Although a large body of work are devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. I...
Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, ...
NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 3 days ago
Learning Dynamic Bayesian Networks
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...
Zoubin Ghahramani
NIPS
2008
13 years 9 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...