Active learning for regression via density power divergence

Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio

研究成果: Contribution to journalArticle査読

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.

本文言語英語
ページ(範囲)13-21
ページ数9
ジャーナルTransactions of the Japanese Society for Artificial Intelligence
28
1
DOI
出版ステータス出版済み - 2013
外部発表はい

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人工知能

フィンガープリント

「Active learning for regression via density power divergence」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル