509 search results - page 38 / 102 » Using Learning for Approximation in Stochastic Processes |
104
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FUZZIEEE
2007
IEEE
15 years 8 months ago
2007
IEEE
— Reinforcement learning (RL) is a learning control paradigm that provides well-understood algorithms with good convergence and consistency properties. Unfortunately, these algor...
158
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CORR
2012
Springer
13 years 10 months ago
2012
Springer
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
109
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INFOCOM
2010
IEEE
15 years 27 days ago
2010
IEEE
— Quantiles are very useful in characterizing the data distribution of an evolving dataset in the process of data mining or network monitoring. The method of Stochastic Approxima...
124
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IJCNN
2007
IEEE
15 years 8 months ago
2007
IEEE
— Optimal component analysis (OCA) uses a stochastic gradient optimization process to find optimal representations for general criteria and shows good performance in object reco...
121
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ICML
2007
IEEE
16 years 3 months ago
2007
IEEE
We introduce a new inference algorithm for Dirichlet process mixture models. While Gibbs sampling and variational methods focus on local moves, the new algorithm makes more global...