Learning With Auxiliary Less-Noisy Labels.
IEEE Transactions on Neural Networks and Learning Systems(2017)
摘要
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier can be difficult due to the limited access to reliable label resources. Instead, in real-world applications, less-accurate labels, such as labels from nonexpert labelers, are often used. However, learning with less-accurate labels can lead to serious performance deterioration because of the high noise...
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关键词
Noise measurement,Training data,Maximum likelihood estimation,Learning systems,Training,Crowdsourcing,Probabilistic logic
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