Output Divergence Criterion for Active Learning in Collaborative Settings
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概要
- 論文の詳細を見る
In this paper, we address the task of active learning for linear regression models in collaborative settings. The goal of active learning is to select training points that would allow accurate prediction of test output values. We propose a new active learning criterion that is aimed at directly improving the accuracy of the output value estimation by analyzing the effect of the new training points on the estimates of the output values. The advantages of the proposed method are highlighted in collaborative settings-where most of the data points are missing, and the number of training data points is much smaller than the number of the parameters of the model.
- 一般社団法人情報処理学会の論文
- 2008-12-10
著者
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Rubens Neil
電気通信大学大学院情報システム学研究科
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Tomioka Ryota
Department Of Computer Science Tokyo Institute Of Technology
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Neil Rubens
Graduate School Of Information Systems University Of Electro-communications
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Rubens Neil
Department Of Computer Science Tokyo Institute Of Technology
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Sugiayama Masashi
Department of Computer Science, Tokyo Institute of Technology
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Sugiayama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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- Output Divergence Criterion for Active Learning in Collaborative Settings
- Output Divergence Criterion for Active Learning in Collaborative Settings