NDLI: Inference of regular languages using model simplicity

Content Provider	IEEE Xplore Digital Library
Author	Hingston, P.
Copyright Year	2001
Abstract	We describe an approach that is related to a number of existing algorithms for the inference of a regular language from a set of positive (and optionally also negative) examples. Variations on this approach provide a family of algorithms that attempt to minimise the complexity of a description of the example data in terms of a finite state automaton model. Experiments using a standard set of small problems show that this approach produces satisfactory results when positive examples only are given, and can be helpful when only a limited number of negative examples is available. The results also suggest that improved algorithms will be needed in order to tackle more challenging problems, such as data mining and exploratory sequential analysis applications.
Sponsorship	Bond Univ
Starting Page	69
Ending Page	76
File Size	823253
Page Count	8
File Format	PDF
ISBN	0769509630
ISSN	15300900
DOI	10.1109/ACSC.2001.906625
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-01-29
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Inference algorithms Automata Data mining Sequential analysis Artificial intelligence Pattern recognition Convergence Heuristic algorithms
Content Type	Text
Resource Type	Article

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Inference of regular languages using model simplicity