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  • Titel: Inductive Inference and Reverse Mathematics
  • Beteiligte: Hölzl, Rupert [VerfasserIn]; Jain, Sanjay [VerfasserIn]; Stephan, Frank [VerfasserIn]
  • Erschienen: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2015
  • Sprache: Englisch
  • DOI: https://doi.org/10.4230/LIPIcs.STACS.2015.420
  • Schlagwörter: learning from positive data ; reverse mathematics ; inductive inference ; recursion theory
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  • Beschreibung: The present work investigates inductive inference from the perspective of reverse mathematics. Reverse mathematics is a framework which relates the proof strength of theorems and axioms throughout many areas of mathematics in an interdisciplinary way. The present work looks at basic notions of learnability including Angluin's tell-tale condition and its variants for learning in the limit and for conservative learning. Furthermore, the more general criterion of partial learning is investigated. These notions are studied in the reverse mathematics context for uniformly and weakly represented families of languages. The results are stated in terms of axioms referring to domination and induction strength.
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