Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-030-68195-1_12
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dc.titleLearnability and Positive Equivalence Relations
dc.contributor.authorBelanger, D
dc.contributor.authorGao, Z
dc.contributor.authorJain, S
dc.contributor.authorLi, W
dc.contributor.authorStephan, F
dc.date.accessioned2023-07-21T08:36:00Z
dc.date.available2023-07-21T08:36:00Z
dc.date.issued2021-01-01
dc.identifier.citationBelanger, D, Gao, Z, Jain, S, Li, W, Stephan, F (2021-01-01). Learnability and Positive Equivalence Relations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12638 LNCS : 145-156. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-030-68195-1_12
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/243314
dc.description.abstractPrior work of Gavryushkin, Khoussainov, Jain and Stephan investigated what algebraic structures can be realised in worlds given by a positive (= recursively enumerable) equivalence relation which partitions the natural numbers into infinitely many equivalence classes. The present work investigates the infinite one-one numbered recursively enumerable (r.e.) families realised by such relations and asks how the choice of the equivalence relation impacts the learnability properties of these classes when studying learnability in the limit from positive examples, also known as learning from text. For all choices of such positive equivalence relations, for each of the following entries, there are one-one numbered r.e. families which satisfy it: (a) they are behaviourally correctly learnable but not vacillatorily learnable; (b) they are explanatorily learnable but not confidently learnable; (c) they are not behaviourally correctly learnable. Furthermore, there is a positive equivalence relation which enforces that (d) every vacillatorily learnable one-one numbered family of languages closed under this equivalence relation is already explanatorily learnable and cannot be confidently learnable.
dc.publisherSpringer International Publishing
dc.sourceElements
dc.subjectcs.LO
dc.subjectcs.LO
dc.typeArticle
dc.date.updated2023-07-21T05:44:28Z
dc.contributor.departmentDEAN'S OFFICE (SCHOOL OF COMPUTING)
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1007/978-3-030-68195-1_12
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume12638 LNCS
dc.description.page145-156
dc.published.statePublished
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