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May, 2017
有信心样本的学习:用于带有噪声标签的强鲁棒分类的排名剪枝
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
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Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
TL;DR
使用Rank Pruning算法解决嘈杂的正负样本学习问题,并且可以估计噪声率,并在MNIST数据集上取得了最先进的噪声估计和分类性能。
Abstract
noisy pn learning
is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose
rank pruning
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