Can PAC learning algorithms tolerate random attribute noise?

DOI10.1007/BF01300374zbMath0837.68094OpenAlexW2021885139MaRDI QIDQ1894713

Sally A. Goldman, Robert H. Sloan

Publication date: 16 April 1996

Published in: Algorithmica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01300374

zbMATH Keywords

PAC learning algorithmsuniform attribute noise

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Parallel algorithms in computer science (68W10)

Related Items (8)

Uniform-distribution attribute noise learnability ⋮ Learning juntas in the presence of noise ⋮ Knowing what doesn't matter: exploiting the omission of irrelevant data ⋮ Incentive compatible regression learning ⋮ Four types of noise in data for PAC learning ⋮ Learning with unreliable boundary queries ⋮ On the Evolution of Monotone Conjunctions: Drilling for Best Approximations ⋮ Learning fixed-dimension linear thresholds from fragmented data

Cites Work

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• Equivalence of models for polynomial learnability
• Learning in the Presence of Malicious Errors
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• Probability Inequalities for Sums of Bounded Random Variables

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