Systems that learn: an introduction to learning theory for cognitive and computer scientists
Systems that learn: an introduction to learning theory for cognitive and computer scientists
Some results in the theory of effective program synthesis: learning by defective information
Proceedings of the International Spring School on Mathematical method of specification and synthesis of software systems '85
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Prudence and other conditions on formal language learning
Information and Computation
An easy priority-free proof of a theorem of Friedberg
Theoretical Computer Science
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
The Power of Vacillation in Language Learning
SIAM Journal on Computing
Characterization Problems in the Theory of Inductive Inference
Proceedings of the Fifth Colloquium on Automata, Languages and Programming
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Variations on U-shaped learning
Information and Computation
Information and Computation
Non-U-shaped vacillatory and team learning
Journal of Computer and System Sciences
Prescribed learning of r.e. classes
Theoretical Computer Science
Hypothesis Spaces for Learning
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Numberings optimal for learning
Journal of Computer and System Sciences
Incremental learning with temporary memory
Theoretical Computer Science
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Hypothesis spaces for learning
Information and Computation
Theoretical Computer Science
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In this paper we consider learnability in some special numberings, such as Friedberg numberings, which contain all the recursively enumerable languages, but have simpler grammar equivalence problem compared to acceptable numberings. We show that every explanatorily learnable class can be learnt in some Friedberg numbering. However, such a result does not hold for behaviourally correct learning or finite learning. One can also show that some Friedberg numberings are so restrictive that all classes which can be explanatorily learnt in such Friedberg numberings have only finitely many infinite languages. We also study similar questions for several properties of learners such as consistency, conservativeness, prudence, iterativeness and non-U-shaped learning. Besides Friedberg numberings, we also consider the above problems for programming systems with K-recursive grammar equivalence problem.