Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Exact K-nearest neighbors"'
Publikováno v:
In Knowledge-Based Systems 15 February 2020 189
Publikováno v:
In Pattern Recognition 2010 43(6):2351-2358
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-55 (2024)
Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performa
Externí odkaz:
https://doaj.org/article/47afd56c6b464b078637a5b751e3bc57
Publikováno v:
Knowledge-Based Systems. 189:105088
The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition, regression, outlier detection and other data mining areas. However, it suffers from the large distance computation cost, especially when dealing with big data applica
Akademický článek
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Publikováno v:
IJCNN
The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-m
Autor:
Wang X; X. Wang is with the Department of Mathematics and Computer Science, Northwest Nazarene University, Nampa, ID 83642 USA.
Publikováno v:
Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks [Proc Int Jt Conf Neural Netw] 2012 Feb 08; Vol. 43 (6), pp. 2351-2358.
Autor:
Xueyi, Wang
Publikováno v:
Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks. 43(6)
The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-m
Autor:
Wang
Publikováno v:
The 2011 International Joint Conference on Neural Networks.
Autor:
Xueyi Wang
Publikováno v:
2011 International Joint Conference on Neural Networks (IJCNN); 2011, p1293-1299, 7p