Chapter 5
The k-Nearest Neighbors Classifiers
5.1 Introduction
In pattern recognition, the k-nearest neighbors algorithm (or k-NN for short) is a nonparametric method used for classification and regression [1]. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:
■ In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k-NN (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
■ In k-NN regression, the output is the property ...
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