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Learning imprecise hidden Markov models

Arthur Van Camp (UGent)
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Abstract
We present a method for learning imprecise local uncertainty models in stationary hidden Markov models. If there is enough data to justify precise local uncertainty models, then existing learning algorithms, such as the Baum–Welch algorithm, can be used. When there is not enough evidence to justify precise models, the method we suggest here has a number of interesting features.

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Please use this url to cite or link to this publication:

Chicago
Van Camp, Arthur. 2011. “Learning Imprecise Hidden Markov Models.” In BENE@WORK, Presentations.
APA
Van Camp, Arthur. (2011). Learning imprecise hidden Markov models. BENE@WORK, Presentations. Presented at the BENE@WORK : 1st workshop for PhD students from Belgium and The Netherlands working on Probabilistic Graphical Models.
Vancouver
1.
Van Camp A. Learning imprecise hidden Markov models. BENE@WORK, Presentations. 2011.
MLA
Van Camp, Arthur. “Learning Imprecise Hidden Markov Models.” BENE@WORK, Presentations. 2011. Print.
@inproceedings{2019788,
  abstract     = {We present a method for learning imprecise local uncertainty models in stationary hidden Markov models. If there is enough data to justify precise local uncertainty models, then existing learning algorithms, such as the Baum--Welch algorithm, can be used. When there is not enough evidence to justify precise models, the method we suggest here has a number of interesting features.},
  author       = {Van Camp, Arthur},
  booktitle    = {BENE@WORK, Presentations},
  language     = {eng},
  location     = {Brussels, Belgium},
  title        = {Learning imprecise hidden Markov models},
  url          = {http://bnatwork.etro.vub.ac.be/bnatwork/WORKSHOP.ashx},
  year         = {2011},
}