The most robust loss function for boosting

Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

研究成果: Article査読

11 被引用数 (Scopus)

抄録

Boosting algorithm is understood as the gradient descent algorithm of a loss function. It is often pointed out that the typical boosting algorithm, Adaboost, is seriously affected by the outliers. In this paper, loss functions for robust boosting are studied. Based on a concept of the robust statistics, we propose a positive-part-truncation of the loss function which makes the boosting algorithm robust against extreme outliers. Numerical experiments show that the proposed boosting algorithm is useful for highly noisy data in comparison with other competitors.

本文言語English
ページ(範囲)496-501
ページ数6
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3316
出版ステータスPublished - 2004 12 1

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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