Learning in Friedberg numberings

DOI10.1016/j.ic.2008.03.001zbMath1152.68024OpenAlexW2111414521MaRDI QIDQ939445

Frank Stephan, Sanjay Jain

Publication date: 22 August 2008

Published in: Information and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ic.2008.03.001

zbMATH Keywords

learnability

Mathematics Subject Classification ID

Computational learning theory (68Q32) Formal languages and automata (68Q45) Applications of computability and recursion theory (03D80)

Related Items (5)

Numberings optimal for learning ⋮ Hypothesis spaces for learning ⋮ Incremental learning with temporary memory ⋮ Hypothesis Spaces for Learning ⋮ Prescribed learning of r.e. classes

Cites Work

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• Characterizing language identification in terms of computable numberings
• Monotonic and non-monotonic inductive inference
• Prudence and other conditions on formal language learning
• An easy priority-free proof of a theorem of Friedberg
• When unlearning helps
• Non-U-shaped vacillatory and team learning
• Finding patterns common to a set of strings
• Classical recursion theory. The theory of functions and sets of natural numbers
• Variations on U-shaped learning
• Three theorems on recursive enumeration. I. Decomposition. II. Maximal set. III. Enumeration without duplication
• Inductive Inference and Computable One‐One Numberings
• Inductive inference of formal languages from positive data
• Toward a mathematical theory of inductive inference
• The Power of Vacillation in Language Learning
• Criteria of language learning
• A Machine-Independent Theory of the Complexity of Recursive Functions
• Language identification in the limit

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