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Inductive learning of recurrence-term languages from positive data
P. Watson
In K.P. Jantke and S. Lange, editors, Algorithmic learning for knowledge-based systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 182-196. Springer Verlag, January 1995.Abstract
We show that the class of languages generated by (basic) recurrence-terms is inferable in the limit from positive data, and that such learning may be consistent and conservative, though not in general strong monotonic. This class of languages has neither of the properties of finite thickness and finite elasticity usually used to prove inferability from positive data, so our proof method is the explicit construction of a tell-tale function for the class of recurrence-term languages. Recurrence-terms are of interest because they generate many sequences arising from divergent cases of Knuth-Bendix completion.
Bibtex Record
@incollection{730, author = {P. Watson}, title = {Inductive learning of recurrence-term languages from positive data}, month = {January}, year = {1995}, pages = {182-196}, keywords = {determinacy analysis, Craig interpolants}, note = {}, doi = {}, url = {http://www.cs.kent.ac.uk/pubs/1995/730}, booktitle = {Algorithmic learning for knowledge-based systems}, editor = {K.P. Jantke and S. Lange}, publisher = {Springer Verlag}, series = {Lecture Notes in Artificial Intelligence}, volume = {961}, }