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A new method for learning imprecise hidden Markov models

Arthur Van Camp UGent and Gert De Cooman UGent (2012) Communications in Computer and Information Science. 299(4). p.460-469
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
imprecise Dirichlet model, expected counts, learning, Hidden Markov model
in
Communications in Computer and Information Science
Commun. comput. inf. sci.
editor
Salvatore Greco, Bernadette Bouchon-Meunier, Giulianella Coletti, Benedetto Matarazzo and Ronald R R Yager
volume
299
issue
4
pages
460 - 469
publisher
Springer
place of publication
Berlin, Germany
conference name
14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU - 2012)
conference location
Catania, Italy
conference start
2012-07-09
conference end
2012-07-13
ISSN
1865-0929
DOI
10.1007/978-3-642-31718-7_48
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2986592
handle
http://hdl.handle.net/1854/LU-2986592
date created
2012-09-13 16:35:17
date last changed
2012-09-19 11:17:42
@inproceedings{2986592,
  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 and De Cooman, Gert},
  booktitle    = {Communications in Computer and Information Science},
  editor       = {Greco, Salvatore  and Bouchon-Meunier, Bernadette  and Coletti, Giulianella  and Matarazzo, Benedetto  and R Yager, Ronald R},
  issn         = {1865-0929},
  keyword      = {imprecise Dirichlet model,expected counts,learning,Hidden Markov model},
  language     = {eng},
  location     = {Catania, Italy},
  number       = {4},
  pages        = {460--469},
  publisher    = {Springer},
  title        = {A new method for learning imprecise hidden Markov models},
  url          = {http://dx.doi.org/10.1007/978-3-642-31718-7\_48},
  volume       = {299},
  year         = {2012},
}

Chicago
Van Camp, Arthur, and Gert De Cooman. 2012. “A New Method for Learning Imprecise Hidden Markov Models.” In Communications in Computer and Information Science, ed. Salvatore Greco, Bernadette Bouchon-Meunier, Giulianella Coletti, Benedetto Matarazzo, and Ronald R R Yager, 299:460–469. Berlin, Germany: Springer.
APA
Van Camp, A., & De Cooman, G. (2012). A new method for learning imprecise hidden Markov models. In Salvatore Greco, B. Bouchon-Meunier, G. Coletti, B. Matarazzo, & R. R. R Yager (Eds.), Communications in Computer and Information Science (Vol. 299, pp. 460–469). Presented at the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU - 2012), Berlin, Germany: Springer.
Vancouver
1.
Van Camp A, De Cooman G. A new method for learning imprecise hidden Markov models. In: Greco S, Bouchon-Meunier B, Coletti G, Matarazzo B, R Yager RR, editors. Communications in Computer and Information Science. Berlin, Germany: Springer; 2012. p. 460–9.
MLA
Van Camp, Arthur, and Gert De Cooman. “A New Method for Learning Imprecise Hidden Markov Models.” Communications in Computer and Information Science. Ed. Salvatore Greco et al. Vol. 299. Berlin, Germany: Springer, 2012. 460–469. Print.