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
Saving the phenomena: requirements that inductive inference machines not contradict known data
Information and Computation
On the role of search for learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Computational learning theory: an introduction
Computational learning theory: an introduction
The nature of statistical learning theory
The nature of statistical learning theory
Robust behaviorally correct learning
Information and Computation
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Robust learning aided by context
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Journal of Computer and System Sciences
Machine Learning
Avoiding coding tricks by hyperrobust learning
Theoretical Computer Science
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
AII '86 Proceedings of the International Workshop on Analogical and Inductive Inference
A Refutation of Barzdins' Conjecture
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
A Note on Polynominal-Time Inference of k-Variable Pattern Languages
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Inductive Inference of Recursive Functions: Qualitative Theory
Baltic Computer Science, Selected Papers
Inductive Inference of Recursive Functions: Complexity Bounds
Baltic Computer Science, Selected Papers
Learning all subfunctions of a function
Information and Computation
Learning recursive functions: A survey
Theoretical Computer Science
Dynamically Delayed Postdictive Completeness and Consistency in Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
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A class b of recursive functions is called robustly learnable in the sense I (where I is any success criterion of learning) if not only b itself but even all transformed classes Θ(b), where Θ is any general recursive operator, are learnable in the sense I. It was already shown before, see Fulk (in: 31st Annual IEEE Symposium on Foundation of Computer Science, IEEE Computer Soc. Press, Silver Spring, MD 1990, pp. 405-410), Jain et al. (J. Comput. System Sci. 62 (2001) 178), that for I=Ex (learning in the limit) robust learning is rich in that there are classes being both not contained in any recursively enumerable class of recursive functions and, nevertheless, robustly learnable. For several criteria I, the present paper makes much more precise where we can hope for robustly learnable classes and where we cannot. This is achieved in two ways. First, for I=Ex, it is shown that only consistently learnable classes can be uniformly robustly learnable. Second, some other learning types I are classified as to whether or not they contain rich robustly learnable classes. Moreover, the first results on separating robust learning from uniformly robust learning are derived.