Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914793981476864 |
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| author | Kohler, Michael Krzyzak, Adam Walter, Benjamin |
| author_facet | Kohler, Michael Krzyzak, Adam Walter, Benjamin |
| contents | Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possible value is derived. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent Kohler, Michael Krzyzak, Adam Walter, Benjamin Machine Learning Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possible value is derived. |
| title | Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.07619 |