Convergence to nearly minimal size grammars by vacillating learning machines
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Learning in the presence of partial explanations
Information and Computation
Learning with the knowledge of an upper bound on program size
Information and Computation
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Inductive Inference from Good Examples
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Synthesizing noise-tolerant language learners
Theoretical Computer Science
Generalization and specialization strategies for learning r.e. languages
Annals of Mathematics and Artificial Intelligence
The Intrinsic Complexity of Learning: A Survey
Fundamenta Informaticae
Inductive inference and computable numberings
Theoretical Computer Science
The Intrinsic Complexity of Learning: A Survey
Fundamenta Informaticae
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Notions from formal language learning theory are characterized in terms of standardizing operations on classes of recursively enumerable languages. Algorithmic identification in the limit of grammars from text presentation of recursively enumerable languages is a central paradigm of language learning. A mapping, F, from the set of all grammars into the set of all grammars is a standardizing operation on a class of recursively enumerable languages @? just in case F maps any grammar for any language L @? @? to a canonical grammar for L. Investigating connections between these two notions is the subject of this paper.