509 search results - page 37 / 102 » Using Learning for Approximation in Stochastic Processes |
142
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JMLR
2012
13 years 4 months ago
2012
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
104
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ICML
2007
IEEE
16 years 3 months ago
2007
IEEE
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
114
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GECCO
2006
Springer
15 years 6 months ago
2006
Springer
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
103
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ASMTA
2009
Springer
15 years 6 months ago
2009
Springer
Discrete stochastic models (DSM) are widely used in various application fields today. Proxel-based simulation can outperform discrete event-based approaches in the analysis of smal...
258
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ECAI
2010
Springer
15 years 3 months ago
2010
Springer
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...