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Sample size lower bounds in PAC learning by Algorithmic Complexity Theory

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Publication:1274920
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DOI10.1016/S0304-3975(97)00102-3zbMath0912.68162OpenAlexW2043360510MaRDI QIDQ1274920

Bruno Apolloni, Claudio Gentile

Publication date: 12 January 1999

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-3975(97)00102-3

zbMATH Keywords

Kolmogorov complexitycomputational learningsample complexity


Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)


Related Items

Unnamed Item, Improved lower bounds for learning from noisy examples: An information-theoretic approach



Cites Work

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  • Learnability with respect to fixed distributions
  • Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
  • Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
  • A Markovian extension of Valiant's learning model
  • On the density of families of sets
  • Learnability and the Vapnik-Chervonenkis dimension
  • A theory of the learnable
  • Randomness conservation inequalities; information and independence in mathematical theories
  • Probably Approximate Learning over Classes of Distributions
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