Synthesizing Learners Tolerating Computable Noisy Data
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Learning Algebraic Structures from Text Using Semantical Knowledge
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
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
Increasing the power of uniform inductive learners
Journal of Computer and System Sciences - Special issue on COLT 2002
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We consider programs that accept descriptions of inductive inference problems and return machines that solve them. Several design specifications for synthesizers of this kind are considered from a recursion-theoretic perspective.