On shallow feedforward neural networks with inputs from a topological space
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917216188891136 |
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| author | Ismailov, Vugar |
| author_facet | Ismailov, Vugar |
| contents | We study feedforward neural networks with inputs from a topological space (TFNNs). We prove a universal approximation theorem for shallow TFNNs, which demonstrates their capacity to approximate any continuous function defined on this topological space. As an application, we obtain an approximative version of Kolmogorov's superposition theorem for compact metric spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On shallow feedforward neural networks with inputs from a topological space Ismailov, Vugar Machine Learning Functional Analysis We study feedforward neural networks with inputs from a topological space (TFNNs). We prove a universal approximation theorem for shallow TFNNs, which demonstrates their capacity to approximate any continuous function defined on this topological space. As an application, we obtain an approximative version of Kolmogorov's superposition theorem for compact metric spaces. |
| title | On shallow feedforward neural networks with inputs from a topological space |
| topic | Machine Learning Functional Analysis |
| url | https://arxiv.org/abs/2504.02321 |