2020
Towards a New Understanding of the Training of Neural Networks with Mislabeled Training Data
ICASSP 2020accepted
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 sa…