110 search results - page 6 / 22 » Learning Partially Observable Markov Models from First Passa... |
CORR
2008
Springer
13 years 7 months ago
2008
Springer
Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computation...
INFOCOM
2012
IEEE
11 years 9 months ago
2012
IEEE
—In this paper we develop an adaptive learning algorithm which is approximately optimal for an opportunistic spectrum access (OSA) problem with polynomial complexity. In this OSA...
NECO
2007
13 years 6 months ago
2007
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
KDD
2008
ACM
14 years 7 months ago
2008
ACM
Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one ...
NIPS
2001
13 years 8 months ago
2001
We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. State representations that are grounded...