Multiscale convolutional neural networks for vision: based classification of cells
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
| Hi-index | 0.01 |
We present a new method to train the members of a committee of one-hidden-layer neural nets. Instead of training various nets on subsets of the training data we preprocess the training data for each individual model such that the corresponding errors are decor related. On the MNIST digit recognition benchmark set we obtain a recognition error rate of 0.39%, using a committee of 25 one-hidden-layer neural nets, which is on par with state-of-the-art recognition rates of more complicated systems.