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
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learning regular sets from queries and counterexamples
Information and Computation
Inductive inference from all positive and some negative data
Information Processing Letters
On the non-existence of maximal inference degrees for language identification
Information Processing Letters
Language learning with some negative information
Journal of Computer and System Sciences
A note on batch and incremental learnability
Journal of Computer and System Sciences
Learning algebraic structures from text
Theoretical Computer Science - Algorithmic learning theory
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Learning by Switching Type of Information
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
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The central topic of the paper is the learnability of the recursively enumerable subspaces of V驴/V, where V驴 is the standard recursive vector space over the rationals with countably infinite dimension, and V is a given recursively enumerable subspace of V驴. It is shown that certain types of vector spaces can be characterized in terms of learnability properties: V驴/V is behaviourally correct learnable from text iff V is finitely dimensional, V驴/V is behaviourally correct learnable from switching type of information iff V is finite-dimensional, 0-thin, or 1-thin. On the other hand, learnability from an informant does not correspond to similar algebraic properties of a given space. There are 0-thin spaces W1 and W2 such that W1 is not explanatorily learnable from informant and the infinite product (W1)驴 is not behaviourally correct learnable, while W2 and the infinite product (W2)驴 are both explanatorily learnable from informant.