Robust loss functions for boosting

Takafumi Kanamori*, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

*この研究の対応する著者

研究成果: Article査読

34 被引用数 (Scopus)

抄録

Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.

本文言語English
ページ(範囲)2183-2244
ページ数62
ジャーナルNeural Computation
19
8
DOI
出版ステータスPublished - 2007 8

ASJC Scopus subject areas

  • 人文科学(その他)
  • 認知神経科学

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