509 search results - page 47 / 102 » Using Learning for Approximation in Stochastic Processes |
145
Voted
UAI
2008
15 years 3 months ago
2008
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
142
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KI
2007
Springer
15 years 8 months ago
2007
Springer
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-orde...
101
Voted
UAI
2008
15 years 3 months ago
2008
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
113
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GECCO
2006
Springer
15 years 6 months ago
2006
Springer
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
126
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NIPS
1996
15 years 3 months ago
1996
In cellular telephone systems, an important problem is to dynamically allocate the communication resource channels so as to maximize service in a stochastic caller environment. Th...