Uncertainty in Artificial Intelligence
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Efficient Induction of Finite State Automata
Matthew Collins, Jonathan Oliver
Abstract:
This paper introduces a new algorithm for the induction if complex finite state automata from samples of behavior. The algorithm is based on information theoretic principles. The algorithm reduces the search space by many orders of magnitude over what was previously thought possible. We compare the algorithm with some existing induction techniques for finite state automata and show that the algorithm is much superior in both run time and quality of inductions.
Keywords: Induction, finite state automata, FSA, minimum message length, MML.
Pages: 99-107
PS Link:
PDF Link: /papers/97/p99-collins.pdf
BibTex:
@INPROCEEDINGS{Collins97,
AUTHOR = "Matthew Collins and Jonathan Oliver",
TITLE = "Efficient Induction of Finite State Automata",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "99--107"
}


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