  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.
