509 search results - page 11 / 102 » Using Learning for Approximation in Stochastic Processes |
CMSB
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. Starting from a biochemical signalling pathway model expressed in a process algebra enriched with quantitative information we automatically derive both continuous-space a...
AIPS
2007
13 years 9 months ago
2007
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
TNN
1998
13 years 7 months ago
1998
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
AAAI
2000
13 years 8 months ago
2000
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a ...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...