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» Statistical Learning of Arbitrary Computable Classifiers
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CVPR
1999
IEEE
15 years 11 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...
CVPR
2011
IEEE
14 years 1 months ago
Effective 3D Object Detection and Regression Using Probabilistic Segmentation Features in CT Images
3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose ...
Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff
84
Voted
CRV
2008
IEEE
295views Robotics» more  CRV 2008»
15 years 4 months ago
3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis
We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image featu...
Kooksang Moon, Vladimir Pavlovic
ICIAP
2005
ACM
15 years 9 months ago
Interactive, Mobile, Distributed Pattern Recognition
As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative appro...
George Nagy
79
Voted
EUROSYS
2006
ACM
15 years 6 months ago
Automated known problem diagnosis with event traces
Computer problem diagnosis remains a serious challenge to users and support professionals. Traditional troubleshooting methods relying heavily on human intervention make the proce...
Chun Yuan, Ni Lao, Ji-Rong Wen, Jiwei Li, Zheng Zh...