Improving Label Noise Filtering by Exploiting Unlabeled Data

Improving Label Noise Filtering by Exploiting Unlabeled Data

With the significant growth in the scale of data, an increasing amount of training data is available in many machine learning tasks. However, it is difficult to ensure perfect labeling with a large volume of training data. Some labels can be incorrect, resulting in label noise, which could lead to d...

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Hlavní autoři: Donghai Guan, Hongqiang Wei, Weiwei Yuan, Guangjie Han, Yuan Tian, Mohammed Al-Dhelaan, Abdullah Al-Dhelaan
Médium: Článek
Jazyk:English
Vydáno: IEEE 2018-01-01
Edice:IEEE Access
Témata:
Label noise
noise filtering
unlabeled data
soft majority voting
On-line přístup:https://ieeexplore.ieee.org/document/8295034/

Internet

https://ieeexplore.ieee.org/document/8295034/

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