Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Inductive inference with bounded number of mind changes
COLT '89 Proceedings of the second 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
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
The Power of Probabilism in Popperian FINite Learning (extended abstract)
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Probabilistic and Pluralistic Learners with Mind Changes
MFCS '92 Proceedings of the 17th International Symposium on Mathematical Foundations of Computer Science
On learning multiple concepts in parallel
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Capabilities of probabilistic learners with bounded mind changes
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Probability is more powerful than team for language identification from positive data
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Capabilities of fallible FINite learning
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The strength of noninclusions for teams of finite learners (extended abstract)
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Choosing a learning team: a topological approach
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Probabilistic and team PFIN-type learning: general properties
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Hierarchies of probabilistic and team FIN -learning
Theoretical Computer Science
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Taming teams with mind changes
Journal of Computer and System Sciences
Learning Behaviors of Functions
Fundamenta Informaticae
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Learning Behaviors of Functions with Teams
Fundamenta Informaticae
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We show that for every probabilistic FIN-type learner with success ratio greater than 24/49, there is another probabilistic FIN-type learner with success ratio 1/2 that simulates the former. We will also show that this simulation result is tight. We obtain as a consequence of this work a characterization of FIN-type team learning with success ratio between 24/49 and 1/2. We conjecture that the learning capabilities of probabilistic FIN-type learners for probabilities beginning at probability 1/2 are delimited by the sequence 8n/17n-2 for n 2, which has an accumulation point at 8/17.