Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the structure of degrees of inferability
Journal of Computer and System Sciences
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Recursive Properties of Abstract Complexity Classes
Journal of the ACM (JACM)
MFCS '95 Proceedings of the 20th International Symposium on Mathematical Foundations of Computer Science
Inductive Inference of Recursive Functions: Qualitative Theory
Baltic Computer Science, Selected Papers
Learning recursive functions: A survey
Theoretical Computer Science
Amount of Nonconstructivity in Finite Automata
CIAA '09 Proceedings of the 14th International Conference on Implementation and Application of Automata
Amount of nonconstructivity in deterministic finite automata
Theoretical Computer Science
On the amount of nonconstructivity in learning formal languages from positive data
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
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Nonconstructive proofs are a powerful mechanism in mathematics. Furthermore, nonconstructive computations by various types of machines and automata have been considered by e.g., Karp and Lipton [17] and Freivalds [11]. They allow to regard more complicated algorithms from the viewpoint of much more primitive computational devices. The amount of nonconstructivity is a quantitative characterization of the distance between types of computational devices with respect to solving a specific problem. In the present paper, the amount of nonconstructivity in learning of recursive functions is studied. Different learning types are compared with respect to the amount of nonconstructivity needed to learn the whole class of general recursive functions. Upper and lower bounds for the amount of nonconstructivity needed are proved.