Nearest Neighbor Classifier – From Theory to Practice

dc.contributor.author	Torfi, Amirsina	en
dc.date.accessioned	2020-01-11T19:02:55Z	en
dc.date.available	2020-01-11T19:02:55Z	en
dc.date.issued	2020-01-11	en
dc.description.abstract	The 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.identifier.uri	http://hdl.handle.net/10919/96404	en
dc.language.iso	en_US	en
dc.publisher	Machine Learning Mindset	en
dc.rights	Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	en
dc.subject	Machine learning	en
dc.subject	Supervised Learning	en
dc.subject	Nearest Neighbor Algorithm	en
dc.title	Nearest Neighbor Classifier – From Theory to Practice	en
dc.type	Article	en
dc.type	Software	en