Generalization in Machine Learning via Analytical Learning Theory

A novel measure-theoretic theory for machine learning that does not require statistical assumptions is introduced and a new regularization method in deep learning is derived and shown to outperform previous methods in CIFar-10, CIFAR-100, and SVHN.