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CVPR
2006
IEEE
16 years 7 days ago
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
We present an algorithm for jointly learning a consistent bidirectional generative-recognition model that combines top-down and bottom-up processing for monocular 3d human motion ...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
CVPR
2008
IEEE
16 years 8 months ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher
ESWA
2006
103views more  ESWA 2006»
15 years 6 months ago
Model gene network by semi-fixed Bayesian network
Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, and protein or RNA transport. Thus, le...
Tie-Fei Liu, Wing-Kin Sung, Ankush Mittal
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
16 years 6 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
ICML
2007
IEEE
16 years 7 months ago
Parameter learning for relational Bayesian networks
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
Manfred Jaeger