An Improved Nearest Neighbour Classifier
This work addresses image classification for handwritten digits, offering a significant improvement over existing methods, though it appears incremental as it builds on nearest neighbor techniques.
The paper tackles image classification by introducing a windowed nearest neighbor (WNN) classifier, achieving a misclassification rate of 0.42% on the EMNIST dataset, which outperforms humans and shallow ANNs with over 1.3% errors.
A windowed version of the Nearest Neighbour (WNN) classifier for images is described. While its construction is inspired by the architecture of Artificial Neural Networks, the underlying theoretical framework is based on approximation theory. We illustrate WNN on the datasets MNIST and EMNIST of images of handwritten digits. In order to calibrate the parameters of WNN, we first study it on the classical MNIST dataset. We then apply WNN with these parameters to the challenging EMNIST dataset. It is demonstrated that WNN misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow ANNs that both have more than 1.3% of errors.