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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.18710 |
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| _version_ | 1866918071930716160 |
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| 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 |