Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
A SOM-based Fuzzy System and Its Application in Handwritten Digits Recognition
MSE '00 Proceedings of the 2000 International Conference on Microelectronic Systems Education
Improving the error backpropagation algorithm with a modified error function
IEEE Transactions on Neural Networks
Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)
IEEE Transactions on Neural Networks
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In this paper, a kernel-based LVQ classifier in input space is proposed to recognize handwritten digit. Classical Learning Vector Quantization is performed in the input space through Euclidean distance, but it doesn't work well when the input patterns are highly nonlinear. In our model, the kernel method is used to define a new metric of distance in input space so we can get a direct view of the clustering result. At last, we test our model by handwritten digit recognition using MNIST database and it obtains better recognition performance than traditional LVQ.