Improving Importance Estimation in Pool-based Batch Active Learning for Approximate Linear Regression (情報論的学習理論と機械学習)
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概要
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Pool-based batch active learning is aimed at choosing training inputs from a 'pool' of test inputs so that the generalization error is minimized. P-ALICE is a state-of-the-art method that can cope with model misspecification by weighting training samples according to the importance (i.e., the ratio of test and training input densities). However, importance estimation in the original P-ALICE is based on the assumption that the number of training samples to gather is small, which is not always true in practice. In this paper, we propose an alternative scheme for importance estimation based on the inclusion probability, and show its validity through numerical experiments.
- 一般社団法人電子情報通信学会の論文
- 2012-03-05
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol. Tokyo Jpn
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KURIHARA Nozomi
Department of Computer Science, Tokyo Institute of Technology
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SUGIYAMA Sasashi
Department of Computer Science, Tokyo Institute of Technology
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