Abstract
The present paper investigates identification of indexed families of recursively enumerable languages from good examples. In the context of class preserving learning from good text examples, it is shown that the notions of finite and limit identification coincide. On the other hand, these two criteria are different in the context of class comprising learning from good text examples. In the context of learning from good informant examples, finite and limit identification criteria differ for both class preserving and class comprising cases. The above results resolve an open question posed by Lange, Nessel and Wiehagen in a similar study about indexed families of recursive languages.
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Jain, S., Lange, S., Nessel, J. (1997). Learning of R.E. Languages from good examples. In: Li, M., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 1997. Lecture Notes in Computer Science, vol 1316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63577-7_34
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DOI: https://doi.org/10.1007/3-540-63577-7_34
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