# Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization Source: [Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization]() Collection index: [llms.txt]() Data updated: 2025-10-25T18:01:20.823Z Authors: Michael Kohler, Adam Krzyzak, Alisha S\\"anger Published: 2024-04-10T16:07:29.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()