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
Prudence and other conditions on formal language learning
Information and Computation
Relativized topological size of sets of partial recursive functions
Theoretical Computer Science
Synthesizing learners tolerating computable noisy data
Journal of Computer and System Sciences
Learning algebraic structures from text
Theoretical Computer Science - Algorithmic learning theory
Information and Randomness: An Algorithmic Perspective
Information and Randomness: An Algorithmic Perspective
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
On the learnability of vector spaces
Journal of Computer and System Sciences
Topological properties of concept spaces (full version)
Information and Computation
Inferability of closed set systems from positive data
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
String extension learning using lattices
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Confident and consistent partial learning of recursive functions
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
| Hi-index | 0.00 |
In this paper it is studied for which oracles A and which types of A-r.e. matroids the class of all A-r.e. closed sets in the matroid is learnable by an unrelativised learner. The learning criteria considered comprise in particular criteria more general than behaviourally correct learning, namely behaviourally correct learning from recursive texts, partial learning and reliably partial learning. For various natural classes of matroids and learning criteria, characterisations of learnability are obtained.