On uniform learnability of language families
Information Processing Letters
Strong separation of learning classes
Journal of Experimental & Theoretical Artificial Intelligence
How inductive inference strategies discover their errors
Information and Computation
The synthesis of language learners
Information and Computation
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Machine Learning
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
On the Synthesis of Strategies Identifying Recursive Functions
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Merging Uniform Inductive Learners
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Separation of uniform learning classes
Theoretical Computer Science - Special issue: Algorithmic learning theory
Synthesizing inductive expertise
Information and Computation
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
Numberings Optimal for Learning
ALT '08 Proceedings of the 19th 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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The analysis of theoretical learning models is basically concerned with the comparison of identification capabilities in different models. Modifications of the formal constraints affect the quality of the corresponding learners on the one hand and regulate the quantity of learnable classes on the other hand. For many inductive inference models-such as Gold's identification in the limit-the corresponding relationships of learning potential provided by the compatible learners are well-known. Recent work even corroborates the relevance of these relationships by revealing them still in the context of uniform Gold-style learning. Uniform learning is rather concerned with the synthesis of successful learners instead of their mere existence. The subsequent analysis further strengthens the results regarding uniform learning, particularly aiming at the design of methods for increasing the potential of the relevant learners. This demonstrates how to improve given learning strategies instead of just verifying the existence of more powerful uniform learners. For technical reasons these results are achieved using various formal conditions concerning the learnability of unions of uniformly learnable classes. Therefore numerous sufficient properties for the learnability of such unions are presented and illustrated with several examples.