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» Bayesian inference in estimation of distribution algorithms
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IJAR
2010
152views more  IJAR 2010»
13 years 6 months ago
Structural-EM for learning PDG models from incomplete data
Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
Jens D. Nielsen, Rafael Rumí, Antonio Salme...
TSP
2008
105views more  TSP 2008»
13 years 7 months ago
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linea...
Nicolas Dobigeon, Jean-Yves Tourneret, Chein-I Cha...
TASLP
2010
117views more  TASLP 2010»
13 years 2 months ago
Speech Enhancement Using Gaussian Scale Mixture Models
This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the fr...
Jiucang Hao, Te-Won Lee, Terrence J. Sejnowski
AVSS
2008
IEEE
14 years 2 months ago
Person Tracking with Audio-Visual Cues Using the Iterative Decoding Framework
Tracking humans in an indoor environment is an essential part of surveillance systems. Vision based and microphone array based trackers have been extensively researched in the pas...
Shankar T. Shivappa, Mohan M. Trivedi, Bhaskar D. ...
AAAI
1998
13 years 9 months ago
Bayesian Q-Learning
A central problem in learning in complex environmentsis balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of explora...
Richard Dearden, Nir Friedman, Stuart J. Russell