A priori estimates for classification problems using neural networks
E Weinan, Stephan Wojtowytsch · arXiv (Cornell University) · 2020
We consider binary and multi-class classification problems using hypothesis classes of neural networks. For a given hypothesis class, we use Rademacher complexity estimates and direct approximation theorems to obtain a priori error estimates for regularized loss functionals.