A priori estimates for classification problems using neural networks

Abstract

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.

References in corpus (1)

• On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics