Weighting of the k-Nearest-Neighbors

Konstantin Chernoff; Mads Nielsen

doi:10.1109/ICPR.2010.168

Weighting of the k-Nearest-Neighbors

Konstantin Chernoff, Mads Nielsen

Datalogisk Institut

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

12 Citationer (Scopus)

Abstract

This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate that the soft kNN errors obtained through b-CCV can be obtained by applying the weighted kNN in a LOO setting, and that the proposed weighting schemes can decrease the variance and improve the generalization of kNN in a CV setting.

Originalsprog	Engelsk
Titel	2010 20th International Conference on Pattern Recognition (ICPR)
Antal sider	4
Forlag	IEEE
Publikationsdato	2010
Sider	666-669
ISBN (Trykt)	978-1-4244-7542-1
ISBN (Elektronisk)	978-1-4244-7541-4
DOI	https://doi.org/10.1109/ICPR.2010.168
Status	Udgivet - 2010
Begivenhed	20th International Conference on Pattern Recognition - Istanbul, Tyrkiet Varighed: 23 aug. 2010 → 26 aug. 2010

Konference

Konference	20th International Conference on Pattern Recognition
Land/Område	Tyrkiet
By	Istanbul
Periode	23/08/2010 → 26/08/2010

Adgang til dokumentet

10.1109/ICPR.2010.168

Citationsformater

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title = "Weighting of the k-Nearest-Neighbors",

abstract = "This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate that the soft kNN errors obtained through b-CCV can be obtained by applying the weighted kNN in a LOO setting, and that the proposed weighting schemes can decrease the variance and improve the generalization of kNN in a CV setting. ",

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T1 - Weighting of the k-Nearest-Neighbors

AU - Chernoff, Konstantin

AU - Nielsen, Mads

PY - 2010

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N2 - This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate that the soft kNN errors obtained through b-CCV can be obtained by applying the weighted kNN in a LOO setting, and that the proposed weighting schemes can decrease the variance and improve the generalization of kNN in a CV setting.

AB - This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate that the soft kNN errors obtained through b-CCV can be obtained by applying the weighted kNN in a LOO setting, and that the proposed weighting schemes can decrease the variance and improve the generalization of kNN in a CV setting.

U2 - 10.1109/ICPR.2010.168

DO - 10.1109/ICPR.2010.168

M3 - Article in proceedings

SN - 978-1-4244-7542-1

SP - 666

EP - 669

BT - 2010 20th International Conference on Pattern Recognition (ICPR)

PB - IEEE

Y2 - 23 August 2010 through 26 August 2010

ER -