1. Yang, Luyu

2.  Postal service for sorting mails by the postal code written on the envelop  Bank system for processing checks by reading the amount of money using computers

3.  Design artificial neural network for handwritten digits recognition  Develop proposed network using training samples in MNIST database  Achieve good testing result

5. Assume each pixel in the digit image is either black or white, which contains 1 bit information. To convert the binary sequence into decimal system {0, 0.1, …,0.9}, the number of elements should be at least log 10 (2 784 ), approximately 236.0075. Let the higher precision in MATLAB compensate for the gray edge of stroke. Therefore, 237 neurons are used in the hidden layer to store patterns of training samples.

6. In the original 784 pixels, even two samples which are very close under Euclidean norm may represent different digits. So quasi nearest neighbor classifier operates at the output of the hidden layer, where the key features are supposed to be more pronounced.

8. ClassifierTesting Error MLP + Quasi Nearest Neighbor1.75% K-nearest neighbors using Euclidean norm 1 5.0% Principle component analysis 1 3.3% Radial basis network 1 3.6% Support vector machine using Gaussian kernel 1 1.4% 2-layer MLP of 300 units 1 4.7% 3-layer MLP of 300+100 units 1 3.05% 3-layer MLP of 500+150 units 1 2.95% 1.Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.