0000000000178164

AUTHOR

Frank Stephan

0000-0001-9152-1706

showing 3 related works from this author

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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On block pumpable languages

2016

Ehrenfeucht, Parikh and Rozenberg gave an interesting characterisation of the regular languages called the block pumping property. When requiring this property only with respect to members of the language but not with respect to nonmembers, one gets the notion of block pumpable languages. It is shown that these block pumpable are a more general concept than regular languages and that they are an interesting notion of their own: they are closed under intersection, union and homomorphism by transducers; they admit multiple pumping; they have either polynomial or exponential growth.

Discrete mathematicsGeneral Computer ScienceAbstract family of languagesComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)0102 computer and information sciences02 engineering and technology01 natural sciencesCone (formal languages)Pumping lemma for regular languagesTheoretical Computer ScienceCombinatoricsRegular languageIntersection010201 computation theory & mathematicsBlock (programming)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingHomomorphismPumping lemma for context-free languagesComputer Science::Formal Languages and Automata TheoryMathematicsTheoretical Computer Science
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On the relative sizes of learnable sets

1998

Abstract 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 inferable sets become large rather quickly in the standard hierarchies of learnability. On the other hand, the complements of the learnable sets are all large.

General Computer Science0102 computer and information sciencesMachine learningcomputer.software_genre01 natural sciencesMeasure (mathematics)Theoretical Computer ScienceTuring machinesymbols.namesake0101 mathematicsMathematicsBinary treeLearnabilitybusiness.industry010102 general mathematicsInductive inferenceCategoryInductive reasoningMeasureAbstract machine010201 computation theory & mathematicssymbolsArtificial intelligencebusinesscomputerComputer Science(all)Theoretical Computer Science
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