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
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Learning by switching type of information
Information and Computation
Learning families of closed sets in matroids
WTCS'12 Proceedings of the 2012 international conference on Theoretical Computer Science: computation, physics and beyond
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The central topic of the paper is the learnability of the recursively enumerable subspaces of V"~/V, where V"~ is the standard recursive vector space over the rationals with (countably) infinite dimension and V is a given recursively enumerable subspace of V"~. It is shown that certain types of vector spaces can be characterized in terms of learnability properties: V"~/V is behaviourally correct learnable from text iff V is finite-dimensional, V"~/V is behaviourally correct learnable from switching the 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 W"1 and W"2 such that W"1 is not explanatorily learnable from an informant, and the infinite product (W"1)^~ is not behaviourally correct learnable from an informant, while both W"2 and the infinite product (W"2)^~ are explanatorily learnable from an informant.