321 search results - page 35 / 65 » Learning Bayesian Networks with Local Structure |
119
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ICML
2010
IEEE
15 years 3 months ago
2010
IEEE
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
142
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ICML
1995
IEEE
16 years 2 months ago
1995
IEEE
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...
123
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UAI
2000
15 years 3 months ago
2000
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
108
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AI
2008
Springer
15 years 2 months ago
2008
Springer
Local computation in join trees or acyclic hypertrees has been shown to be linked to a particular algebraic structure, called valuation algebra. There are many models of this alge...
111
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IDA
2009
Springer
15 years 8 months ago
2009
Springer
We present a novel extension to Independent Component Analysis (ICA), where the data is generated as the product of two submodels, each of which follow an ICA model, and which comb...