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Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis. [PDF]
PLoS OneDong L, Zuo X, Xiong Y.
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Pattern Recognition, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Timofte, Radu, Van Gool, Luc
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Timofte, Radu, Van Gool, Luc
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Proceedings of the seventh annual symposium on Computational geometry - SCG '91, 1991
Suppose E is a set of labeled points (examples) in some metric space. A subset C of E is said to be a consistent subset ofE if it has the property that for any example e∈E, the label of the closest example in C to e is the same as the label of e. We consider the problem of computing a minimum cardinality consistent subset.
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Suppose E is a set of labeled points (examples) in some metric space. A subset C of E is said to be a consistent subset ofE if it has the property that for any example e∈E, the label of the closest example in C to e is the same as the label of e. We consider the problem of computing a minimum cardinality consistent subset.
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Nearest-neighbor median filter
Applied Optics, 1988A new generalized version of the median filter is proposed. The new version preserves clear definitions of image details while keeping the ability of removing the impulsive noise; it is still simple in principle and easy to use. The input-output relationship and noise-removing power of the new version are compared with those of the standard median ...
K, Itoh, Y, Ichioka, T, Minami
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Kernel Nearest-Neighbor Algorithm
Neural Processing Letters, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu, Kai, Ji, Liang, Zhang, Xuegong
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k_n-nearest neighbor classification
IEEE Transactions on Information Theory, 1972The k_n nearest neighbor classification rule is a nonparametric classification procedure that assigns a random vector Z to one of two populations \pi_1, \pi_2 . Samples of equal size n are taken from \pi_1 and \pi_2 and are ordered separately with respect to their distance from Z = z .
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Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98, 1998
We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living in R d , the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such
Piotr Indyk, Rajeev Motwani
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We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living in R d , the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such
Piotr Indyk, Rajeev Motwani
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1996
Simple rules survive. The k-nearest neighbor rule, since its conception in 1951 and 1952 (Fix and Hodges (1951; 1952; 1991a; 1991b)), has thus attracted many followers and continues to be studied by many researchers.
Luc Devroye +2 more
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Simple rules survive. The k-nearest neighbor rule, since its conception in 1951 and 1952 (Fix and Hodges (1951; 1952; 1991a; 1991b)), has thus attracted many followers and continues to be studied by many researchers.
Luc Devroye +2 more
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2001
The nearest neighbor problem is defined as follows: Given a metric space X and a fixed finite subset S ⊂ X of n “sites”, preprocess S and build a data structure so that queries of the following kind can be answered efficiently: Given a point q ∈ X find one of the points p ∈ S closest to q (see Figure 1). Open image in new window Fig. 1.
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The nearest neighbor problem is defined as follows: Given a metric space X and a fixed finite subset S ⊂ X of n “sites”, preprocess S and build a data structure so that queries of the following kind can be answered efficiently: Given a point q ∈ X find one of the points p ∈ S closest to q (see Figure 1). Open image in new window Fig. 1.
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