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» Hidden Markov Models with Multiple Observation Processes
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ECML
2005
Springer
15 years 5 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
ICMCS
2000
IEEE
138views Multimedia» more  ICMCS 2000»
15 years 4 months ago
Event-Coupled Hidden Markov Models
Inferences from time-series data can be greatly enhanced by taking into account multiple modalities. In some cases, such as audio of speech and the corresponding video of lip gest...
Trausti T. Kristjansson, Brendan J. Frey, Thomas S...
NIPS
2008
15 years 1 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
ECML
2006
Springer
15 years 3 months ago
PAC-Learning of Markov Models with Hidden State
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...
Ricard Gavaldà, Philipp W. Keller, Joelle P...
ARTMED
2000
105views more  ARTMED 2000»
14 years 11 months ago
Planning treatment of ischemic heart disease with partially observable Markov decision processes
Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty...
Milos Hauskrecht, Hamish S. F. Fraser