, Volume 14, Issue 1, pp 70–84

Can PAC learning algorithms tolerate random attribute noise?

  • S. A. Goldman
  • R. H. Sloan

DOI: 10.1007/BF01300374

Cite this article as:
Goldman, S.A. & Sloan, R.H. Algorithmica (1995) 14: 70. doi:10.1007/BF01300374


This paper studies the robustness of PAC learning algorithms when the instance space is {0,1}n, and the examples are corrupted by purely random noise affecting only the attributes (and not the labels). Foruniform attribute noise, in which each attribute is flipped independently at random with the same probability, we present an algorithm that PAC learns monomials for any (unknown) noise rate less than21. Contrasting this positive result, we show thatproduct random attribute noise, where each attributei is flipped randomly and independently with its own probability pi, is nearly as harmful as malicious noise-no algorithm can tolerate more than a very small amount of such noise.

Key words

PAC learning Attribute noise Computational learning theory 

Copyright information

© Springer-Verlag New York Inc 1995

Authors and Affiliations

  • S. A. Goldman
    • 1
  • R. H. Sloan
    • 2
  1. 1.Department of Computer ScienceWashington UniversitySt. LouisUSA
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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