人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
原著論文
Density Power Divergenceを用いたロバスト能動回帰学習
十河 泰弘植野 剛河原 吉伸鷲尾 隆
著者情報
ジャーナル フリー

2013 年 28 巻 1 号 p. 13-21

詳細
抄録

The accuracy of active learning is crucially influenced by the existence of noisy labels given by a real-world noisy oracle. In this paper, we propose a novel pool-based active learning framework through density power divergence. It is known that density power divergence, such as β -divergence and γ-divergence, can be accurately estimated even under the existence of outliers (noisy labels) within data. In addition, we propose an evaluation scheme for these measures based on those asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation variance. Experiments on artificial and real-world datasets show that our active learning scheme performs better than state-of-the-art methods.

著者関連情報
© 2013 JSAI (The Japanese Society for Artificial Intelligence)
前の記事 次の記事
feedback
Top