Inductive learning of recurrence-term languages from positive data

Watson, Phil (1995) Inductive learning of recurrence-term languages from positive data. In: Jantke, Klaus P. and Lange, Steffen, eds. Algorithmic learning for knowledge-based systems. Lecture Notes in Artificial Intelligence, 961 . Springer Verlag, pp. 292-315. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not available from this repository. (Contact us about this Publication)


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.

Item Type: Book section
Uncontrolled keywords: algorithmic learning theory, recurrence-term languages
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Theoretical Computing Group
Depositing User: Mark Wheadon
Date Deposited: 24 Aug 2009 18:26
Last Modified: 23 May 2014 14:07
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):