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CVPR
1999
IEEE
16 years 8 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
ICML
2008
IEEE
16 years 7 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
PERCOM
2004
ACM
16 years 6 months ago
Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
One way to improve inferences on sensor data is to tune the algorithms through a time-consuming offline procedure. A less expensive, and potentially more accurate method is to use...
Ezekiel S. Bhasker, Steven W. Brown, William G. Gr...
ICML
2009
IEEE
16 years 1 months ago
Sparse higher order conditional random fields for improved sequence labeling
In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse H...
Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huan...
3DIM
2007
IEEE
16 years 1 months ago
Generalized MPU Implicits Using Belief Propagation
In this paper, we present a new algorithm to reconstruct 3D surfaces from an unorganized point cloud based on generalizing the MPU implicit algorithm through introducing a powerfu...
Yi-Ling Chen, Shang-Hong Lai, Tung-Ying Lee