Learning of R.E. Languages from Good Examples
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.
Cite
Text
Jain et al. "Learning of R.E. Languages from Good Examples." International Conference on Algorithmic Learning Theory, 1997. doi:10.1007/3-540-63577-7_34Markdown
[Jain et al. "Learning of R.E. Languages from Good Examples." International Conference on Algorithmic Learning Theory, 1997.](https://mlanthology.org/alt/1997/jain1997alt-learning/) doi:10.1007/3-540-63577-7_34BibTeX
@inproceedings{jain1997alt-learning,
title = {{Learning of R.E. Languages from Good Examples}},
author = {Jain, Sanjay and Lange, Steffen and Nessel, Jochen},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {1997},
pages = {32-47},
doi = {10.1007/3-540-63577-7_34},
url = {https://mlanthology.org/alt/1997/jain1997alt-learning/}
}