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Selective sampling using the query by committee algorithm

delete1997-01-01
delete803
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OA
AI
Y
Yoav Freund *
H
H. Sebastian Seung
E
Eli Shamir
N
Naftali Tishby
DOI：10.1023/A:1007330508534delete
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Abstract

Abstract

En 中文
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.
Keywords:
selective sampling
query learning
Bayesian Learning
experimental design
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Machine Learning cover
Machine Learning
IF:
2.9
Papers:
2.6K
Citations:
3.4W

Organization

No organization information available