509 search results - page 39 / 102 » Using Learning for Approximation in Stochastic Processes |
110
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KESAMSTA
2009
Springer
15 years 9 months ago
2009
Springer
Abstract. Imitative learning can be considered an essential task of humans development. People use instructions and demonstrations provided by other human experts to acquire knowle...
111
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UAI
2003
15 years 3 months ago
2003
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...
AAAI
2006
15 years 3 months ago
2006
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
107
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SARA
2005
Springer
15 years 8 months ago
2005
Springer
Sets of features in Markov decision processes can play a critical role ximately representing value and in abstracting the state space. Selection of features is crucial to the succe...
109
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AIPS
2003
15 years 3 months ago
2003
Recommender systems — systems that suggest to users in e-commerce sites items that might interest them — adopt a static view of the recommendation process and treat it as a pr...