Classification with Noisy Labels by Importance Reweighting

In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability <inline-formula><tex-math>...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 38; H. 3; S. 447 - 461
Hauptverfasser:	Liu, Tongliang, Tao, Dacheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Classification consistency Convergence Estimation importance reweighting Kernel label noise Noise Noise measurement noise rate estimation Robustness label noise noise rate estimation importance reweighting consistency Classification
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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