Non-parametric Nearest Neighbor with Local Adaptation

The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach...

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Detalles Bibliográficos
Autores: Ferrer Troyano, Francisco Javier, Aguilar Ruiz, Jesús Salvador, Riquelme Santos, José Cristóbal
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2001
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/39147
Acceso en línea:http://hdl.handle.net/11441/39147
https://doi.org/10.1007/3-540-45329-6_6
Access Level:acceso abierto
Palabra clave:Artificial Intelligence (incl. Robotics)
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spelling Non-parametric Nearest Neighbor with Local AdaptationFerrer Troyano, Francisco JavierAguilar Ruiz, Jesús SalvadorRiquelme Santos, José CristóbalArtificial Intelligence (incl. Robotics)The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified most examples. As the user does not take part in the election of the parameter k, the algorithm is non-parametric. With this heuristic, we propose an easy variation of the k-NN algorithm that gives robustness with noise present in data. Summarized in the last section, the experiments show that the error rate decreases in comparison with the k-NN technique when the best k for each database has been previously obtained.Lenguajes y Sistemas Informáticos2001info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39147https://doi.org/10.1007/3-540-45329-6_6reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésProgress in Artificial Intelligence, Notes in Computer Science, Volume 2258, pp 22-29 (2001)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/391472026-06-17T12:51:07Z
dc.title.none.fl_str_mv Non-parametric Nearest Neighbor with Local Adaptation
title Non-parametric Nearest Neighbor with Local Adaptation
spellingShingle Non-parametric Nearest Neighbor with Local Adaptation
Ferrer Troyano, Francisco Javier
Artificial Intelligence (incl. Robotics)
title_short Non-parametric Nearest Neighbor with Local Adaptation
title_full Non-parametric Nearest Neighbor with Local Adaptation
title_fullStr Non-parametric Nearest Neighbor with Local Adaptation
title_full_unstemmed Non-parametric Nearest Neighbor with Local Adaptation
title_sort Non-parametric Nearest Neighbor with Local Adaptation
dc.creator.none.fl_str_mv Ferrer Troyano, Francisco Javier
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author Ferrer Troyano, Francisco Javier
author_facet Ferrer Troyano, Francisco Javier
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author_role author
author2 Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Artificial Intelligence (incl. Robotics)
topic Artificial Intelligence (incl. Robotics)
description The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified most examples. As the user does not take part in the election of the parameter k, the algorithm is non-parametric. With this heuristic, we propose an easy variation of the k-NN algorithm that gives robustness with noise present in data. Summarized in the last section, the experiments show that the error rate decreases in comparison with the k-NN technique when the best k for each database has been previously obtained.
publishDate 2001
dc.date.none.fl_str_mv 2001
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/39147
https://doi.org/10.1007/3-540-45329-6_6
url http://hdl.handle.net/11441/39147
https://doi.org/10.1007/3-540-45329-6_6
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Progress in Artificial Intelligence, Notes in Computer Science, Volume 2258, pp 22-29 (2001)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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