Abstract
We study learning of indexable families of recursive languages from good examples. We show that this approach is considerably more powerful than learning from all examples and point out reasons for this additional power. We present several characterizations of types of learning from good examples. We derive similarities as well as differences to learning of recursive functions from good examples.
This work has been partially supported by the German Ministry for Research and Technology (BMFT) under contract no. 413-4001-01 IW 101 A and E
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© 1994 Springer-Verlag Berlin Heidelberg
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Lange, S., Nessel, J., Wiehagen, R. (1994). Language learning from good examples. In: Arikawa, S., Jantke, K.P. (eds) Algorithmic Learning Theory. AII ALT 1994 1994. Lecture Notes in Computer Science, vol 872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58520-6_81
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DOI: https://doi.org/10.1007/3-540-58520-6_81
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Online ISBN: 978-3-540-49030-2
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