On universal learning algorithms

DOI10.1016/S0020-0190(97)00117-8zbMath1337.68144OpenAlexW1969622410MaRDI QIDQ287154

Oded Goldreich, Dana Ron

Publication date: 26 May 2016

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

Full work available at URL: https://doi.org/10.1016/s0020-0190(97)00117-8

zbMATH Keywords

PAC-learningtheorey of computation

Mathematics Subject Classification ID

Computational learning theory (68Q32) Analysis of algorithms and problem complexity (68Q25)

Related Items (1)

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Cites Work

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• Results on learnability and the Vapnik-Chervonenkis dimension
• Learnability with respect to fixed distributions
• One way functions and pseudorandom generators
• A general lower bound on the number of examples needed for learning
• Queries and concept learning
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• Efficient noise-tolerant learning from statistical queries

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