509 search results - page 69 / 102 » Using Learning for Approximation in Stochastic Processes |
134
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NIPS
1996
15 years 3 months ago
1996
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
139
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ICMLA
2010
15 years 8 days ago
2010
Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their traini...
117
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ICML
2005
IEEE
16 years 3 months ago
2005
IEEE
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
122
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NIPS
2001
15 years 3 months ago
2001
Tangential hand velocity profiles of rapid human arm movements often appear as sequences of several bell-shaped acceleration-deceleration phases called submovements or movement un...
161
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IPPS
2010
IEEE
15 years 7 days ago
2010
IEEE
Abstract--The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov C...