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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
International Workshop All '86 on Analogical and inductive inference
On the role of search for learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Robust learning aided by context
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
On the power of learning robustly
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Avoiding Coding Tricks by Hyperrobust Learning
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Transformations that Preserve Learnability
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Inductive Inference of Recursive Functions: Qualitative Theory
Baltic Computer Science, Selected Papers
Robust separations in inductive inference
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
One-sided error probabilistic inductive inference and reliable frequency identification
Information and Computation
Research in the theory of inductive inference by GDR mathematicians-A survey
Information Sciences: an International Journal
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
| Hi-index | 0.00 |
Blum and Blum (1975) showed that a class B of suitable recursive approximations to the halting problem is reliably EX-learnable. These investigations are carried on by showing that B is neither in NUM nor robustly EX-learnable. Since the definition of the class B is quite natural and does not contain any self-referential coding, B serves as an example that the notion of robustness for learning is quite more restrictive than intended. Moreover, variants of this problem obtained by approximating any given recursively enumerable set A instead of the halting problem K are studied. All corresponding function classes U(A) are still EX-inferable but may fail to be reliably EX-learnable, for example if A is non-high and hypersimple. Additionally, it is proved that U(A) is neither in NUM nor robustly EX-learnable provided A is part of a recursively inseparable pair, A is simple but not hypersimple or A is neither recursive nor high. These results provide more evidence that there is still some need to find an adequate notion for "naturally learnable function classes."