Learnability: Admissible, Co-Finite, and Hypersimple Languages

Presented is a surprising characterization of hypersimple sets in algorithmic learning theory. It is used herein to obtain an elegant, tight separation result for learnability criteria. It is argued that such separation results may yield insight for eventual characterizations.

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