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dc.contributor.authorTorfi, Amirsinaen
dc.date.accessioned2020-01-11T19:02:55Zen
dc.date.available2020-01-11T19:02:55Zen
dc.date.issued2020-01-11en
dc.identifier.urihttp://hdl.handle.net/10919/96404en
dc.description.abstractThe K-nearest neighbors (KNNs) classifier or simply Nearest Neighbor Classifier is a kind of supervised machine learning algorithm that operates based on spatial distance measurements. In this article, we investigate the theory behind it. Furthermore, a working example of the k-nearest neighbor classifier will be represented.en
dc.language.isoen_USen
dc.publisherMachine Learning Mindseten
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/en
dc.subjectMachine learningen
dc.subjectSupervised Learningen
dc.subjectNearest Neighbor Algorithmen
dc.titleNearest Neighbor Classifier – From Theory to Practiceen
dc.typeArticleen
dc.typeSoftwareen


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  • Graduate Student Papers [58]
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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States
License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States