Weighting of the k-Nearest-Neighbors
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Weighting of the k-Nearest-Neighbors. / Chernoff, Konstantin; Nielsen, Mads.
2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. p. 666-669.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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