# An Improved Nearest Neighbour Classifier

arXiv: https://arxiv.org/abs/2204.13141 · HTML: https://mlsanity.com/paper/2204.13141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [TextCaps : Handwritten Character Recognition with Very Small Datasets](https://mlsanity.com/paper/1904.08095/) (2019)
- [EDEN: Evolutionary Deep Networks for Efficient Machine Learning](https://mlsanity.com/paper/1709.09161/) (2017)
- [MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric](https://mlsanity.com/paper/1809.06846/) (2018)

## Abstract (arXiv)

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.