# Learning deterministic regular grammars from stochastic samples in polynomial time

Rafael C. Carrasco; Jose Oncina

RAIRO - Theoretical Informatics and Applications (2010)

- Volume: 33, Issue: 1, page 1-19
- ISSN: 0988-3754

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topCarrasco, Rafael C., and Oncina, Jose. "Learning deterministic regular grammars from stochastic samples in polynomial time." RAIRO - Theoretical Informatics and Applications 33.1 (2010): 1-19. <http://eudml.org/doc/221999>.

@article{Carrasco2010,

abstract = {
In this paper, the identification of stochastic
regular languages is addressed.
For this purpose, we propose a class of algorithms which
allow
for the identification of the structure
of the minimal stochastic automaton generating the language.
It is shown that the time needed grows only linearly with the size of the
sample set and a measure of the complexity of the task is provided.
Experimentally, our implementation proves very fast
for application
purposes.
},

author = {Carrasco, Rafael C., Oncina, Jose},

journal = {RAIRO - Theoretical Informatics and Applications},

keywords = {stochastic regular languages},

language = {eng},

month = {3},

number = {1},

pages = {1-19},

publisher = {EDP Sciences},

title = {Learning deterministic regular grammars from stochastic samples in polynomial time},

url = {http://eudml.org/doc/221999},

volume = {33},

year = {2010},

}

TY - JOUR

AU - Carrasco, Rafael C.

AU - Oncina, Jose

TI - Learning deterministic regular grammars from stochastic samples in polynomial time

JO - RAIRO - Theoretical Informatics and Applications

DA - 2010/3//

PB - EDP Sciences

VL - 33

IS - 1

SP - 1

EP - 19

AB -
In this paper, the identification of stochastic
regular languages is addressed.
For this purpose, we propose a class of algorithms which
allow
for the identification of the structure
of the minimal stochastic automaton generating the language.
It is shown that the time needed grows only linearly with the size of the
sample set and a measure of the complexity of the task is provided.
Experimentally, our implementation proves very fast
for application
purposes.

LA - eng

KW - stochastic regular languages

UR - http://eudml.org/doc/221999

ER -

## References

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