Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
| Autor: | Kohler, Michael, Krzyzak, Adam, Sänger, Alisha |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Image classification from independent and identically distributed random variables is considered. Image classifiers are defined which are based on a linear combination of deep convolutional networks with max-pooling layer. Here all the weights are learned by stochastic gradient descent. A general result is presented which shows that the image classifiers are able to approximate the best possible deep convolutional network. In case that the a posteriori probability satisfies a suitable hierarchical composition model it is shown that the corresponding deep convolutional neural network image classifier achieves a rate of convergence which is independent of the dimension of the images. Comment: arXiv admin note: text overlap with arXiv:2312.17007 |
| Databáze: | arXiv |
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