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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.
Overregularization seen in child language learning, re verb tense constructs, involves abandoning correct behaviors for incorrect ones and later reverting to correct behaviors. Quite a number of other child development phenomena also follow this U-shaped form of learning, unlearning, and relearning. A decisive learner doesn’t do this and, in general, never abandons an hypothesis H for an inequivalent...
A generator program for a computable function (by definition) generates an infinite sequence of programs all but finitely many of which compute that function. Machine learning of generator programs for computable functions is studied. To partially motivate these studies, it is shown that, in some cases, interesting global properties for computable functions can be proved from suitable generator programs...
Most theoretical studies of inductive inference model a situation involving a machine M learning its environment E on following lines. M, placed in E, receives data about E, and simultaneously conjectures a sequence of hypotheses. M is said to learn E just in case the sequence of hypotheses conjectured by M stabilizes to a final hypothesis which correctly represents E. The above model makes...
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