Selective Sampling Using the Query by Committee Algorithm

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

doi:10.1023/a:1007330508534

Selective Sampling Using the Query by Committee Algorithm

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

Research output: Contribution to journal › Article › peer-review

897 Scopus citations

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 perceptions.

Original language	English (US)
Pages (from-to)	133-168
Number of pages	36
Journal	Machine Learning
Volume	28
Issue number	2-3
DOIs	https://doi.org/10.1023/a:1007330508534
State	Published - 1997
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Keywords

Bayesian Learning
Experimental design
Query learning
Selective sampling

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10.1023/a:1007330508534

Cite this

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keywords = "Bayesian Learning, Experimental design, Query learning, Selective sampling",

author = "Yoav Freund and Seung, {H. Sebastian} and Eli Shamir and Naftali Tishby",

year = "1997",

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language = "English (US)",

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AU - Freund, Yoav

AU - Seung, H. Sebastian

AU - Shamir, Eli

AU - Tishby, Naftali

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Bayesian Learning

KW - Experimental design

KW - Query learning

KW - Selective sampling

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EP - 168

JO - Machine Learning

JF - Machine Learning

IS - 2-3

ER -

Selective Sampling Using the Query by Committee Algorithm

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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