509 search results - page 16 / 102 » Using Learning for Approximation in Stochastic Processes |
CORR
2010
Springer
13 years 7 months ago
2010
Springer
We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the ...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
ICGI
1998
Springer
13 years 11 months ago
1998
Springer
We present in this paper a new learning problem called learning distributions from experts. In the case we study the experts are stochastic deterministic finite automata (sdfa). W...
JMLR
2010
13 years 2 months ago
2010
In this paper, we consider the asymptotic form of the generalization error for the restricted Boltzmann machine in Bayesian estimation. It has been shown that obtaining the maximu...
ICPR
2010
IEEE
13 years 11 months ago
2010
IEEE
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to...