Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Information Science
RAS ID
1893
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
DOI
10.1109/ACSC.2001.906625
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Hingston, P. F. (2001). Inference of regular languages using model simplicity. Proceedings of 2001 Australian Computer Science Conference. (pp. 69-76). Gold Coast, QLD. IEEE. Available here
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