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Fast learning of restricted regular expressions and DTDs
conference contributionposted on 2017-09-05, 15:35 authored by Dominik FreydenbergerDominik Freydenberger, Timo Kotzing
We study the problem of generalizing from a finite sample to a language taken from a predefined language class. The two language classes we consider are subsets of the regular languages and have significance in the specification of XML documents (the classes corresponding to so called chain regular expressions, Chares, and to single occurrence regular expressions, Sores). The previous literature gave a number of algorithms for generalizing to Sores providing a trade off between quality of the solution and speed. Furthermore, a fast but nonoptimal algorithm for generalizing to Chares is known. For each of the two language classes we give an efficient algorithm returning a minimal generalization from the given finite sample to an element of the fixed language class; such generalizations are called descriptive. In this sense, both our algorithms are optimal. Copyright 2013 ACM.
- Computer Science
Published inACM International Conference Proceeding Series
Pages45 - 56
CitationFREYDENBERGER, D.D. and KÖTZING, T., 2013. Fast learning of restricted regular expressions and DTDs. ACM International Conference Proceeding Series, pp.45-56
Publisher© Association for Computing Machinery, Inc.
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Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper is closed access.