  Machine learning techniques are used in a wide range of domains. However,
machine learning models often suffer from the problem of over-fitting. Many
data augmentation methods have been proposed to tackle such a problem, and one
of them is called mixup. Mixup is a recently proposed regularization procedure,
which linearly interpolates a random pair of training examples. This
regularization method works very well experimentally, but its theoretical
guarantee is not adequately discussed. In this study, we aim to discover why
mixup works well from the aspect of the statistical learning theory.
