Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

中文翻译：

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k-最近邻分类器 - 教程

也许最直接的分类器或机器学习技术是最近邻分类器——分类是通过识别查询示例的最近邻居并使用这些邻居来确定查询的类别来实现的。这种分类方法特别重要，因为在当今可用的计算能力下，运行时性能差的问题不再是一个问题。本文概述了最近邻分类的技术，重点是：评估相似性（距离）的机制、识别最近邻的计算问题以及减少数据维度的机制。本文是之前作为技术报告发表的一篇论文的第二版 [16]。关于时间序列相似性度量的部分，检索加速，并增加了内在维度。包含一个附录，提供对关键方法的 Python 代码的访问。