Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
On the power of inductive inference from good examples
Theoretical Computer Science
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
A learning-theoretic characterization of classes of recursive functions
Information Processing Letters
Journal of Computer and System Sciences
Learning recursive languages from good examples
Annals of Mathematics and Artificial Intelligence
Learning of R.E. Languages from Good Examples
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
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The paper investigates whether it is possible to learn every enumerable classes of recursive functions from "typical" examples. "Typical" means, there is a computable family of finite sets, such that for each function in the class there is one set of examples that can be used in any suitable hypothesis space for this class of functions. As it will turn out, there are enumerable classes of recursive functions that are not learnable from "typical" examples. The learnable classes are characterized. The results are proved within an abstract model of learning from examples, introduced by Freivalds, Kinber and Wiehagen. Finally, the results are interpreted and possible connections of this theoretical work to the situation in real life classrooms are pointed out.