A priori estimates for classification problems using neural networks
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Optimal Approximation Rate of ReLU Networks in terms of Width and Depthcited by 95
- 2021Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neuronscited by 41
- 2025Nonlocal techniques for the analysis of deep ReLU neural network approximationscited by 2
- 2021Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networkscited by 0
- 2024Dimension-independent learning rates for high-dimensional classification problemscited by 0
- 2025Nonlocal techniques for the analysis of deep ReLU neural network approximations7 Apr 2025
- 2024Dimension-independent learning rates for high-dimensional classification problems26 Sep 2024
- 2021Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons6 Jul 2021
- 2021Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks30 Mar 2021
- 2021Optimal Approximation Rate of ReLU Networks in terms of Width and Depth28 Feb 2021