k-Nearest Neighbour Classifiers
|Title:||k-Nearest Neighbour Classifiers||Authors:||Cunningham, Pádraig; Delany, Sarah Jane||Permanent link:||http://hdl.handle.net/10197/12360||Date:||27-Mar-2007||Online since:||2021-07-29T16:15:02Z||Abstract:||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 today because issues of poor run-time performance is not such a problem these days with the computational power that is available. 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.||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; UCD-CSI-2007-3||Copyright (published version):||2007 the Authors||Keywords:||Nearest Neighbour classification; Machine learning; Computational complexity; Dimension reduction||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science and Informatics Technical Reports|
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