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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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 3; s. 447 - 461
Hlavní autori:	Liu, Tongliang, Tao, Dacheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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