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» First-order probabilistic inference
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122
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JMLR
2010
143views more  JMLR 2010»
14 years 7 months ago
Beware of the DAG!
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
A. Philip Dawid
88
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GECCO
2007
Springer
201views Optimization» more  GECCO 2007»
15 years 6 months ago
A parallel framework for loopy belief propagation
There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them....
Alexander Mendiburu, Roberto Santana, Jose Antonio...
108
Voted
MLDM
2007
Springer
15 years 6 months ago
Transductive Learning from Relational Data
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
Michelangelo Ceci, Annalisa Appice, Nicola Barile,...
DT
2006
98views more  DT 2006»
15 years 17 days ago
Handling variations and uncertainties
The widely used engineering decisions concerning the performance of technological equipment for process industries are usually deterministic. Since the early 1990s probabilistic m...
Tim Cheng
129
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AAAI
2008
15 years 2 months ago
Structure Learning on Large Scale Common Sense Statistical Models of Human State
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
William Pentney, Matthai Philipose, Jeff A. Bilmes