Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
- Resource Type
- Working Paper
- Authors
- Kohler, Michael; Krzyzak, Adam; Sänger, Alisha
- Source
- Subject
- Mathematics - Statistics Theory
- Language
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