A gaussian process robust regression

Noboru Murata, Yusuke Kuroda

研究成果: Article査読

抄録

A modified Gaussian process regression is proposed aiming at making regressors robust against outliers. The proposed method is based on U-loss, which is introduced as a natural extension of Kullback-Leibler divergence. The robustness is examined based on the influence function, and numerical experiments are conducted for contaminated data sets and it is shown that the practical performance agrees with the theoretical analysis.

本文言語English
ページ(範囲)280-283
ページ数4
ジャーナルProgress of Theoretical Physics Supplement
157
DOI
出版ステータスPublished - 2005

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

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