The most robust loss function for boosting

Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)496-501
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3316
Publication statusPublished - 2004 Dec 1

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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