Instance-based Learning: k-Nearest Neighbour

Citation:
Wagacha PW. "Instance-based Learning: k-Nearest Neighbour." Foundations of Learning and Adaptive Systems Course. 2003.

Abstract:

The nearest neighbour classifier is one of the simplest and oldest methods for performing
general, non-parametric classification. It can be represented by the following rule: to classify
an unknown pattern, choose the class of the nearest example in the training set as
measured by a distance metric. A common extension is to choose the most common class in
the k nearest neighbours. Despite its simplicity, the nearest neighbour classifier has many
advantages over other methods as well as disadvantages.

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