Selective Sampling Using the Query by Committee Algorithm

Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby

Research output: Contribution to journalArticlepeer-review

878 Scopus citations


We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of perceptions.

Original languageEnglish (US)
Pages (from-to)133-168
Number of pages36
JournalMachine Learning
Issue number2-3
StatePublished - 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


  • Bayesian Learning
  • Experimental design
  • Query learning
  • Selective sampling


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