53 search results - page 3 / 11 » Learning first-order Markov models for control |
NIPS
2001
14 years 11 months ago
2001
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integ...
94
Voted
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP...
COLT
2000
Springer
15 years 2 months ago
2000
Springer
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approache...
DATE
2008
IEEE
15 years 4 months ago
2008
IEEE
- The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning proce...
IROS
2009
IEEE
15 years 4 months ago
2009
IEEE
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...