Machine learning
K-Nearest Neighbors
K-Nearest Neighbors (KNN)，由 Cover 和 Hart 于 1967 年正式提出，是一种非参数、基于实例的方法，它通过查看训练数据中 k 个最近的样本来对新观测进行分类或预测。对于分类，它在这些邻居中进行多数投票；对于回归，它计算这些邻居值的平均数。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI: 10.1109/TIT.1967.1053964 ↗
如何引用本页
ScholarGate. (2026, June 1). K-Nearest Neighbors (KNN) Classification and Regression. ScholarGate. https://scholargate.app/zh/machine-learning/knn
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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