509 search results - page 1 / 102 » Using Learning for Approximation in Stochastic Processes |
ICML
1998
IEEE
14 years 5 months ago
1998
IEEE
ICML
1999
IEEE
14 years 5 months ago
1999
IEEE
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
ICGI
2004
Springer
13 years 10 months ago
2004
Springer
Abstract. Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of th...
SIAMCO
2000
13 years 4 months ago
2000
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
WSC
2008
13 years 7 months ago
2008
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...