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
On the power of inductive inference from good examples
Theoretical Computer Science
On the role of procrastination in machine learning
Information and Computation
Vacillatory learning of nearly minimal size grammars
Journal of Computer and System Sciences
Characterizations of monotonic and dual monotonic language learning
Information and Computation
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
Monotonic and dual monotonic language learning
Theoretical Computer Science
Generalized notions of mind change complexity
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Incremental concept learning for bounded data mining
Information and Computation
On the learnability of recursively enumerable languages from good examples
Theoretical Computer Science
Learning recursive languages from good examples
Annals of Mathematics and Artificial Intelligence
On the power of incremental learning
Theoretical Computer Science
Mind change complexity of learning logic programs
Theoretical Computer Science
Ordinal Mind Change Complexity of Language Identification
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
On Monotonic Strategies for Learning r.e. Languages
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Learning, Logic, and Topology in a Common Framework
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
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We investigate regular tree languages' exact learning from positive examples and membership queries. Input data are trees of the language to infer. The learner computes new trees from the inputs and asks the oracle whether or not they belong to the language. ...