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ICCV
2005
IEEE
15 years 11 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
ECTEL
2007
Springer
15 years 3 months ago
Model Driven E-Learning Platform Integration
The success of the e-learning paradigm observed in recent times created a growing demand for e-learning systems in universities and other educational institutions, that itself led ...
Zuzana Bizonova
ATAL
2010
Springer
14 years 10 months ago
PAC-MDP learning with knowledge-based admissible models
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
Marek Grzes, Daniel Kudenko
ICMCS
2005
IEEE
92views Multimedia» more  ICMCS 2005»
15 years 3 months ago
Semi-supervised meeting event recognition with adapted HMMs
This paper investigates the use of unlabeled data to help labeled data for audio-visual event recognition in meetings. To deal with situations in which it is difficult to collect...
Dong Zhang, Daniel Gatica-Perez, Samy Bengio
CVPR
2004
IEEE
15 years 11 months ago
Model-Based Motion Clustering Using Boosted Mixture Modeling
Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with dif...
Vladimir Pavlovic