509 search results - page 32 / 102 » Using Learning for Approximation in Stochastic Processes |
118
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JMLR
2002
15 years 2 months ago
2002
Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
123
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PKDD
2009
Springer
15 years 9 months ago
2009
Springer
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
122
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JAIR
2011
14 years 9 months ago
2011
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making proble...
131
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NECO
2007
15 years 1 months ago
2007
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
115
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CORR
2010
Springer
15 years 2 months ago
2010
Springer
For a sensor network, as tractable spatially-dependent node deployment model is presented with the property that the density is inversely proportional to the sink distance. A stoc...