Machine Learning

, Volume 28, Issue 2, pp 133–168

Selective Sampling Using the Query by Committee Algorithm

  • Yoav Freund
  • H. Sebastian Seung
  • Eli Shamir
  • Naftali Tishby
Article

DOI: 10.1023/A:1007330508534

Cite this article as:
Freund, Y., Seung, H.S., Shamir, E. et al. Machine Learning (1997) 28: 133. doi:10.1023/A:1007330508534

Abstract

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 perceptrons.

selective samplingquery learningBayesian Learningexperimental design
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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Yoav Freund
    • 1
  • H. Sebastian Seung
    • 2
  • Eli Shamir
    • 3
  • Naftali Tishby
    • 3
  1. 1.AT&T LabsFlorham Park
  2. 2.Bell LaboratoriesLucent Technologies
  3. 3.Institute of Computer ScienceHebrew UniversityJerusalemISRAEL