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Abstract: A new algorithm for learning one-variable pattern languages from positive data is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into account all operations till convergence to a correct hypothesis is achieved. For almost all meaningful distributions defining how the pattern variable is replaced by a string to generate random examples of the target pattern language, it is shown that this algorithm converges within an expected ... (Update)
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BibTeX entry: (Update)
R. Reischuk and T. Zeugmann, An average-case optimal one-variable pattern language learner. Journal of Computer and System Sciences 60 (2000) 302--335. http://citeseer.ist.psu.edu/reischuk00averagecase.html More
@article{ reischuk98averagecase,
author = "Rudiger Reischuk and Thomas Zeugmann",
title = "An Average-Case Optimal One-Variable Pattern Language Learner",
journal = "Electronic Colloquium on Computational Complexity (ECCC)",
volume = "5",
number = "69",
year = "1998",
url = "citeseer.ist.psu.edu/reischuk00averagecase.html" }
Citations (may not include all citations):537 A Theory of the Learnable (context) - Valiant - 1984
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31 the Complexity of Inductive Inference (context) - Daley, Smith - 1986
24 Text Algorithms (context) - Crochmore, Rytter - 1994
23 Bayesian Inductive Logic Programming - Muggleton - 1994
23 Ignoring Data may be the only Way to Learn Efficiently - Wiehagen, Zeugmann - 1994
22 DFAs and Computational Complexity (context) - Pitt, Inference - 1989
[Article contains additional citations not shown here]
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