Title:	Nearest Neighbour Classifier
DNr:	LiU-compute-2021-41
Project Type:	LiU Compute
Principal Investigator:	Natan Kruglyak <natan.kruglyak@liu.se>
Affiliation:	Linköpings universitet
Duration:	2021-09-09 – 2021-11-30
Classification:	10199
Keywords:

Abstract

A windowed version of the Nearest Neighbour (NN) classifier for images is investigated. While its construction is inspired by the architecture of artificial neural networks, the underlying theoretical framework comes from approximation theory. The well known MNIST dataset of handwritten digits, and its recent extension EMNIST, are used to investigate the described concepts. In combination with extensions through shifts, rotations, and non-uniform scalings of the available training images, this novel classifier is expected to outperform previously published NN algorithms on these datasets.