Publication | Closed Access
Breaking the probability ½ barrier in FIN-type learning
61
Citations
13
References
1992
Year
Unknown Venue
Artificial IntelligenceProbability ½ BarrierEngineeringMachine LearningComputational Learning TheoryEntropyFin-type TeamGame TheoryAlgorithmic LearningImprecise ProbabilityGame-theoretic ProbabilityProbabilistic Fin-type LearnerProbabilistic ComputationProbability TheoryComputer ScienceRandomized AlgorithmSuccess Ratio
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.
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