7311 search results - page 119 / 1463 » Time in State Machines |
147
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ICML
1994
IEEE
15 years 7 months ago
1994
IEEE
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
134
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CAV
1998
Springer
15 years 8 months ago
1998
Springer
This paper presents a new algorithm for efficiently verifying timed systems. The new algorithm represents timing information using geometric regions and explores the timed state sp...
127
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ICML
2008
IEEE
16 years 5 months ago
2008
IEEE
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
121
Voted
ECML
2006
Springer
15 years 7 months ago
2006
Springer
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...
129
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CSCLP
2006
Springer
15 years 7 months ago
2006
Springer
Abstract. Traditional resources in scheduling are simple machines where a capacity is the main restriction. However, in practice there frequently appear resources with more complex...