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Abstract: In this paper, we study the performance of active learning with the query algorithm Query by Committee (QBC), which selects a new query such that it approximately maximizes the expected information gain. As target functions, we introduce a generalization of the High-Low-Game, for which we derive a theoretically optimal query sequence. (Update)
Active bibliography (related documents): More All
0.2: Active Learning of the Generalized High-Low-Game - Martina Hasenjäger, Helge.. (1996) (Correct)
0.2: Active Learning in Neural Networks - Hasenjäger, Ritter (Correct)
0.2: Statistical Active Learning in Multilayer Perceptrons - Fukumizu (2000) (Correct)
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BibTeX entry: (Update)
@misc{ ger-active,
author = "Martina Hasenjäger and Helge Ritter",
title = "Active Learning of the Generalized High-Low-Game",
url = "citeseer.ist.psu.edu/669608.html" }
Citations (may not include all citations):83 Query by committee - Seung, Opper et al. - 1992
20 and query by committee (context) - Freund, Seung et al. - 1993
5 entropy and generalization in neural network models (context) - Sollich - 1994
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