On the learnability of monotone \(k\mu\)-DNF formulae under product distributions

DOI10.1016/0020-0190(94)00138-3zbMath0820.68103OpenAlexW2033489088MaRDI QIDQ1338785

Michele Flammini

Publication date: 20 November 1994

Published in: Information Processing Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0190(94)00138-3

zbMATH Keywords

analysis of algorithmscomputational learningconcept classboolean formulaeDNF formulae

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Proper learning of \(k\)-term DNF formulas from satisfying assignments

Cites Work

• Fast probabilistic algorithms for Hamiltonian circuits and matchings
• On learning monotone DNF formulae under uniform distributions
• A general lower bound on the number of examples needed for learning
• A theory of the learnable
• Computational limitations on learning from examples

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