Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Synthesizing inductive expertise
Information and Computation
Saving the phenomena: requirements that inductive inference machines not contradict known data
Information and Computation
On uniform learnability of language families
Information Processing Letters
How inductive inference strategies discover their errors
Information and Computation
The synthesis of language learners
Information and Computation
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Increasing the power of uniform inductive learners
Journal of Computer and System Sciences - Special issue on COLT 2002
Learning recursive functions: A survey
Theoretical Computer Science
Prescribed Learning of Indexed Families
Fundamenta Informaticae
Prescribed Learning of R.E. Classes
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Prescribed learning of r.e. classes
Theoretical Computer Science
Numberings optimal for learning
Journal of Computer and System Sciences
Prescribed Learning of Indexed Families
Fundamenta Informaticae
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Within the scope of inductive inference a recursion theoretic approach is used to model learning behaviour. The fundamental model considered is Gold's identification of recursive functions in the limit. Modifying the corresponding definition has proposed several inference classes, which have been compared regarding the capacities of the relevant learners. The present paper is concerned with a meta-version of this learning model. Given a description of a class of target functions, a uniform learner is supposed to develop a specific successful method for learning the represented class. The same modifications as in the elementary model are considered in the context of uniform learning, especially respecting identification capacities. It turns out that the former separations of inference classes are reflected on the meta-level, in particular finite classes of recursive functions--which constitute the most simple learning problems in the elementary model--are evidence of these separations.