Before revision:  Lyu and Li described a sense in which neural networks, under certain conditions, are equivalent to SVM with the after kernel. Our experiments are consistent with this proposition under natural conditions.
After revision:  For some dataset-architecture pairs, after a few epochs of neural network training, a hard-margin SVM using the network's after kernel is much more accurate than when the network's initial kernel is used.