Explicit neural network classifiers for non-separable data
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918071930716160 |
|---|---|
| author | Ewald, Patrícia Muñoz |
| author_facet | Ewald, Patrícia Muñoz |
| contents | We fully characterize a large class of feedforward neural networks in terms of truncation maps. As an application, we show how a ReLU neural network can implement a feature map which separates concentric data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explicit neural network classifiers for non-separable data Ewald, Patrícia Muñoz Machine Learning Artificial Intelligence Optimization and Control 68T07 We fully characterize a large class of feedforward neural networks in terms of truncation maps. As an application, we show how a ReLU neural network can implement a feature map which separates concentric data. |
| title | Explicit neural network classifiers for non-separable data |
| topic | Machine Learning Artificial Intelligence Optimization and Control 68T07 |
| url | https://arxiv.org/abs/2504.18710 |