2025年1月29日 · K-Nearest Neighbors (KNN) is a classification algorithm that predicts the category of a new data point based on the majority class of its K closest neighbors in the training dataset, utilizing distance metrics like Euclidean, Manhattan, and …

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] . Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership.

在模式识别领域中，最近鄰居法（KNN算法，又譯K-近邻算法）是一种用于分类和回归的無母數統計方法 [1] ，由美国统计学家伊芙琳·费克斯和小約瑟夫·霍奇斯于1951年首次提出，后来由 托馬斯·寇弗 （ 英语 ： Thomas M. Cover ） 扩展。

k 近邻法 (k-nearest neighbor, k-NN） 是一种基本分类与回归方法。是数据挖掘技术中原理最简单的算法之一，核心功能是解决有监督的分类问题。KNN能够快速高效地解决建立在特殊数据集上的预测分类问题，但其不产生…

2019年11月1日 · We introduce k NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k -nearest neighbors (k NN) model. The nearest …

k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. kNN groups the data into coherent clusters or subsets and classifies the newly inputted data based on its similarity with ...

2024年11月20日 · k-近邻算法（K-Nearest Neighbors, KNN）是一种基于实例的学习方法，其核心思想是通过比较待分类样本与训练集中样本之间的相似性来进行分类或回归。以下将详细阐述k-近邻算法的基本原理，包括其工作机制、距离度量、k值的选择以及优缺点。

The k-nearest neighbors (KNN) algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. It is one of the popular and simplest classification and regression classifiers used in machine learning today.

2020年4月9日 · This paper 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 paper is the second edition of a paper previously published as a technical report.

The k-nearest neighbors (kNN) algorithm (Cover et al., 1967) is a very simple nonparametric algorithm widely used for classification and regression. kNN is an instance-based learner (also known as lazy learning) that does not train a classification model until provided with samples to classify (Kotsiantis, 2007). The main principle of kNN ...