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» Bayesian Learning in Undirected Graphical Models: Approximat...
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EMNLP
2009
13 years 4 months ago
On the Use of Virtual Evidence in Conditional Random Fields
Virtual evidence (VE), first introduced by (Pearl, 1988), provides a convenient way of incorporating prior knowledge into Bayesian networks. This work generalizes the use of VE to...
Xiao Li
NIPS
2007
13 years 7 months ago
Bayesian Co-Training
We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumption...
Shipeng Yu, Balaji Krishnapuram, Rómer Rosa...
BMCBI
2010
178views more  BMCBI 2010»
13 years 6 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
JMLR
2010
141views more  JMLR 2010»
13 years 1 months ago
FastInf: An Efficient Approximate Inference Library
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
CVPR
2010
IEEE
12 years 3 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu