APPLICATION OF SICoNNETS TO HANDWRITTEN DIGIT RECOGNITION
Fok Hing Chi Tivive, Abdesselam Bouzerdoum · International Journal of Computational Intelligence and Applications · 2006
In this paper, we apply a new neural network model, namely shunting inhibitory convolutional neural networks, or SICoNNets for short, to the problem of handwritten digit recognition. This type of networks has a generic and flexible architecture, where the processing is based on the physiologically plausible mechanism of shunting inhibition. A hybrid first-order training method, called QRProp, is developed based on the three training algorithms Rprop, Quickprop, and SuperSAB. The MNIST database is used to train and evaluate the performance of SICoNNets in handwritten digit recognition. A network with 24 feature maps and 2722 free parameters achieves a recognition accuracy of 97.3%.