Towards Understanding Generalization via Analytical Learning Theory
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the propose...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en_US |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/118307 |