On learning from queries and counterexamples in the presence of noise

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Recently Angluin and Laird have introduced the classification noise process in the Valiant learnability model and proposed an interesting problem to explore the effect of noise in a situation that calls for queries as well as random sampling. In this paper, we present a general method to modify a polynomial-time learning algorithm from a sampling oracle and membership queries to compensate for random errors in the sampling and query responses.

Original languageEnglish
Pages (from-to)279-284
Number of pages6
JournalInformation Processing Letters
Issue number5
Publication statusPublished - 1991 Mar 14
Externally publishedYes


  • Concept learning
  • analysis of algorithms
  • formal languages
  • noise
  • queries
  • random sampling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Computer Science Applications


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