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Weighting of the k-Nearest-Neighbors

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Weighting of the k-Nearest-Neighbors. / Chernoff, Konstantin; Nielsen, Mads.

2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. s. 666-669.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Chernoff, K & Nielsen, M 2010, Weighting of the k-Nearest-Neighbors. i 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, s. 666-669, 20th International Conference on Pattern Recognition, Istanbul, Tyrkiet, 23/08/2010. https://doi.org/10.1109/ICPR.2010.168

APA

Chernoff, K., & Nielsen, M. (2010). Weighting of the k-Nearest-Neighbors. I 2010 20th International Conference on Pattern Recognition (ICPR) (s. 666-669). IEEE. https://doi.org/10.1109/ICPR.2010.168

Vancouver

Chernoff K, Nielsen M. Weighting of the k-Nearest-Neighbors. I 2010 20th International Conference on Pattern Recognition (ICPR). IEEE. 2010. s. 666-669 https://doi.org/10.1109/ICPR.2010.168

Author

Chernoff, Konstantin ; Nielsen, Mads. / Weighting of the k-Nearest-Neighbors. 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. s. 666-669

Bibtex

@inproceedings{a617c540f16911dfb6d2000ea68e967b,
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.",
author = "Konstantin Chernoff and Mads Nielsen",
year = "2010",
doi = "10.1109/ICPR.2010.168",
language = "English",
isbn = "978-1-4244-7542-1",
pages = "666--669",
booktitle = "2010 20th International Conference on Pattern Recognition (ICPR)",
publisher = "IEEE",
note = "null ; Conference date: 23-08-2010 Through 26-08-2010",

}

RIS

TY - GEN

T1 - Weighting of the k-Nearest-Neighbors

AU - Chernoff, Konstantin

AU - Nielsen, Mads

PY - 2010

Y1 - 2010

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 -

ID: 172801044