2868 search results - page 86 / 574 » Inference in Bayesian Networks |
145
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JMLR
2002
15 years 6 months ago
2002
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...
185
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CVPR
2004
IEEE
16 years 8 months ago
2004
IEEE
Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targe...
214
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ESOP
2011
Springer
14 years 9 months ago
2011
Springer
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
177
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ICONIP
2007
15 years 7 months ago
2007
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
137
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ICML
2008
IEEE
16 years 7 months ago
2008
IEEE
What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version...