Inferring probabilistic acyclic automata using the minimum description length principle

Yoram Singer; Naftali Tishby

doi:10.1109/ISIT.1994.394627

Inferring probabilistic acyclic automata using the minimum description length principle

Yoram Singer, Naftali Tishby

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The use of Rissanen's (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.

Original language	English (US)
Title of host publication	Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994
Number of pages	1
DOIs	https://doi.org/10.1109/ISIT.1994.394627
State	Published - Dec 1 1994
Externally published	Yes
Event	1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, Norway Duration: Jun 27 1994 → Jul 1 1994

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
ISSN (Print)	2157-8095

Other

Other	1994 IEEE International Symposium on Information Theory, ISIT 1994
Country/Territory	Norway
City	Trondheim
Period	6/27/94 → 7/1/94

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.1994.394627

Cite this

@inproceedings{d10f7f029e2b47e49812e8f88ce2ed7b,

title = "Inferring probabilistic acyclic automata using the minimum description length principle",

abstract = "The use of Rissanen's (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.",

author = "Yoram Singer and Naftali Tishby",

year = "1994",

month = dec,

day = "1",

doi = "10.1109/ISIT.1994.394627",

language = "English (US)",

isbn = "0780320158",

series = "IEEE International Symposium on Information Theory - Proceedings",

booktitle = "Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994",

note = "1994 IEEE International Symposium on Information Theory, ISIT 1994 ; Conference date: 27-06-1994 Through 01-07-1994",

}

Singer, Y & Tishby, N 1994, Inferring probabilistic acyclic automata using the minimum description length principle. in Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994., 394627, IEEE International Symposium on Information Theory - Proceedings, 1994 IEEE International Symposium on Information Theory, ISIT 1994, Trondheim, Norway, 6/27/94. https://doi.org/10.1109/ISIT.1994.394627

Inferring probabilistic acyclic automata using the minimum description length principle. / Singer, Yoram; Tishby, Naftali.
Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994. 1994. 394627 (IEEE International Symposium on Information Theory - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Inferring probabilistic acyclic automata using the minimum description length principle

AU - Singer, Yoram

AU - Tishby, Naftali

PY - 1994/12/1

Y1 - 1994/12/1

N2 - The use of Rissanen's (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.

AB - The use of Rissanen's (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.

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DO - 10.1109/ISIT.1994.394627

M3 - Conference contribution

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SN - 0780320158

SN - 9780780320154

T3 - IEEE International Symposium on Information Theory - Proceedings

BT - Proceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994

T2 - 1994 IEEE International Symposium on Information Theory, ISIT 1994

Y2 - 27 June 1994 through 1 July 1994

ER -

Inferring probabilistic acyclic automata using the minimum description length principle

Abstract

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All Science Journal Classification (ASJC) codes

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