Learning One-Variable Pattern Languages in Linear Average Time

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Learning One-Variable Pattern Languages in Linear Average Time
A new algorithm for learning one-variable pattern languages is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into account all operations till an algorithm has converged to a correct hypothesis. For the expectation it is shown that for almost all meaningful distributions defining how the pattern variable is replaced by a string to generate random examples of the target pattern language this algorithm converges within a constant number of rounds with a total learning time that is linear in the pattern length. Thus, the algorithm is average-case optimal in a strong sense. Though one-variable pattern languages cannot be inferred finitely, our approach can also be considered as probabilistic finite learning with high confidence.
Rüdiger Reischuk, Thomas Zeugmann
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where COLT
Authors Rüdiger Reischuk, Thomas Zeugmann
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