%0 Journal Article
%T A Simple CW-SSIM Kernel-based Nearest Neighbor Method for Handwritten Digit Classification
%A Jiheng Wang
%A Guangzhe Fan
%A Zhou Wang
%J Statistics
%D 2010
%I arXiv
%X We propose a simple kernel based nearest neighbor approach for handwritten digit classification. The "distance" here is actually a kernel defining the similarity between two images. We carefully study the effects of different number of neighbors and weight schemes and report the results. With only a few nearest neighbors (or most similar images) to vote, the test set error rate on MNIST database could reach about 1.5%-2.0%, which is very close to many advanced models.
%U http://arxiv.org/abs/1008.3951v3