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Towards a New Understanding of the Training of Neural Networks with Mislabeled Training Data
[article]
2019
arXiv
pre-print
We investigate the problem of machine learning with mislabeled training data. We try to make the effects of mislabeled training better understood through analysis of the basic model and equations that characterize the problem. This includes results about the ability of the noisy model to make the same decisions as the clean model and the effects of noise on model performance. In addition to providing better insights we also are able to show that the Maximum Likelihood (ML) estimate of the
arXiv:1909.09136v1
fatcat:oftptf64kjcs5isslfqqao7dsu