Active learning for noisy oracle via density power divergence

Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio

研究成果: Contribution to journalArticle査読

4 被引用数 (Scopus)

抄録

The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as β-divergence and γ-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods.

本文言語英語
ページ(範囲)133-143
ページ数11
ジャーナルNeural Networks
46
DOI
出版ステータス出版済み - 10 2013
外部発表はい

All Science Journal Classification (ASJC) codes

  • 認知神経科学
  • 人工知能

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