Search results for "Sample exclusion dimension"

showing 2 items of 2 documents

Measure, category and learning theory

1995

Measure and category (or rather, their recursion theoretical counterparts) have been used in Theoretical Computer Science to make precise the intuitive notion “for most of the recursive sets.” We use the notions of effective measure and category to discuss the relative sizes of inferrible sets, and their complements. We find that inferrible sets become large rather quickly in the standard hierarchies of learnability. On the other hand, the complements of the learnable sets are all large.

Preference learningRecursionTheoretical computer scienceLearnabilitySample exclusion dimensionComputer scienceConcept learningAlgorithmic learning theoryMeasure (mathematics)Recursive tree
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Learning formulae from elementary facts

1997

Since the seminal paper by E.M. Gold [Gol67] the computational learning theory community has been presuming that the main problem in the learning theory on the recursion-theoretical level is to restore a grammar from samples of language or a program from its sample computations. However scientists in physics and biology have become accustomed to looking for interesting assertions rather than for a universal theory explaining everything.

Computational learning theoryGrammarSample exclusion dimensionmedia_common.quotation_subjectAlgorithmic learning theoryMathematics educationLearning theoryReinforcement learningSample (statistics)Inductive reasoningmedia_commonMathematics
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