TY - JOUR
T1 - A gaussian process robust regression
AU - Murata, Noboru
AU - Kuroda, Yusuke
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
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U2 - 10.1143/PTPS.157.280
DO - 10.1143/PTPS.157.280
M3 - Article
AN - SCOPUS:22144464448
SN - 0033-068X
VL - 157
SP - 280
EP - 283
JO - Progress of Theoretical Physics
JF - Progress of Theoretical Physics
ER -