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
Probability and plurality for aggregations of learning machines
Information and Computation
Towards a mathematical theory of machine discovery from facts
Theoretical Computer Science - Special issue on algorithmic learning theory
On the structure of degrees of inferability
Journal of Computer and System Sciences
Learning recursive functions from approximations
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Inductive Inference Machines That Can Refute Hypothesis Spaces
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Three Decades of Team Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
On learning of functions refutably
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
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We characterize FIN-, EX- and BC-learning, as well as the corresponding notions of team learning, in terms of isolated branches on effectively given sequences of trees. The more restrictive models of FIN-learning and strong-monotonic BC-learning are characterized in terms of isolated branches on a single tree. Furthermore, we discuss learning with additional information where the learner receives an index for a strongly recursive tree such that the function to be learned is isolated on this tree. We show that EXlearning with this type of additional information is strictly more powerful than EX-learning.