On the rate of convergence of image classifiers based on convolutional neural networks

3 Mar 2020  ·  M. Kohler, A. Krzyzak, B. Walter ·

Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of the aposteriori probability a rate of convergence is shown which is independent of the dimension of the image... This proves that in image classification it is possible to circumvent the curse of dimensionality by convolutional neural networks. read more

PDF Abstract

Datasets


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here