509 search results - page 72 / 102 » Using Learning for Approximation in Stochastic Processes |
136
Voted
ICCV
2003
IEEE
15 years 7 months ago
2003
IEEE
This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion ...
106
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ECML
2004
Springer
15 years 7 months ago
2004
Springer
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
129
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AIIDE
2006
15 years 3 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
144
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EMNLP
2011
14 years 2 months ago
2011
We consider the problem of performing learning and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference...
134
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PE
2010
Springer
15 years 22 days ago
2010
Springer
We consider a push pull queueing system with two servers and two types of jobs which are processed by the two servers in opposite order, with stochastic generally distributed proc...