509 search results - page 27 / 102 » Using Learning for Approximation in Stochastic Processes |
ICASSP
2011
IEEE
14 years 6 months ago
2011
IEEE
Recently there has been much interest in using transmit beamforming to provide multi-antenna physical-layer multicasting. A state of the art in this context is the semidefinite r...
ICML
2006
IEEE
16 years 3 months ago
2006
IEEE
When the transition probabilities and rewards of a Markov Decision Process are specified exactly, the problem can be solved without any interaction with the environment. When no s...
177
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WIOPT
2011
IEEE
14 years 6 months ago
2011
IEEE
—This paper studies the performance of ad hoc networks with local FDMA scheduling using stochastic point processes. In such networks, the Poisson assumption is not justified due...
119
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SAC
2008
ACM
15 years 1 months ago
2008
ACM
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
136
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ICML
1996
IEEE
16 years 3 months ago
1996
IEEE
Some of the most successful recent applications of reinforcement learning have used neural networks and the TD algorithm to learn evaluation functions. In this paper, we examine t...