Communications of the ACM
On the complexity of inductive inference
Information and Control
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
Trade-off among parameters affecting inductive inference
Information and Computation
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Relations between probabilistic and team one-shot learners (extended abstract)
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Breaking the probability ½ barrier in FIN-type learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Journal of the ACM (JACM)
Capabilities of probabilistic learners with bounded mind changes
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Asking Questions Versus Verifiability
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Probabilistic and Pluralistic Learners with Mind Changes
MFCS '92 Proceedings of the 17th International Symposium on Mathematical Foundations of Computer Science
Learning Behaviors of Functions
Fundamenta Informaticae
Learning Behaviors of Functions with Teams
Fundamenta Informaticae
| Hi-index | 0.00 |
This paper is concerned with the algorithmic learning where the learner is allowed to make a finite but bounded number of mind changes. Briefly, in our learning paradigm, a learner is given examples from a recursive function, which the learner attempts to learn by producing programs to compute that function. We say that a team is successful if at least one member of the team learns the target function. The problem, given two teams with bounded number of mind changes whether, one team can provably learn more than the other team, was first proposed by Smith [C.H. Smith, The power of pluralism for automatic program synthesis, J. Assoc. Comput. Mach. 29 (1982) 1144-1165]. This problem has been open for the last twenty five years. This paper makes progress toward a complete solution of this problem. In the case of error-free learning, this paper closes the gap between the lower and the upper bounds. Finally, in the case of EX learning our result shows that there is no team with a=0 mind changes whose learning power is exactly equal to a single learner with bounded b (a) number of mind changes. In the case of Popperian learning (PEX) we have a positive answer.