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Multiple k nearest neighbor search
World Wide Web, 2016The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas ...
Yu-Chi Chung +3 more
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Classification with learning k-nearest neighbors
Proceedings of International Conference on Neural Networks (ICNN'96), 2002The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet most efficient classification rules and are widely used in practice. We introduce three adaptation rules that can be used in iterative training of a k-NN classifier.
Jorma Laaksonen, Erkki Oja
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Improvements in K-Nearest Neighbor Classification
2001We have deveioped two novel methods to improve K-nearest neighbor (K-NN) classifications. First, we introduce a new technique to greatly reduce the template size. This significantly improves classification time with no accuracy drop. Secondly, we introduce a preprocessing procedure to preclude a large part of prototype patterns which are unlikely to ...
Yingquan Wu +2 more
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On reverse-k-nearest-neighbor joins
GeoInformatica, 2014A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has only received little ...
Tobias Emrich +5 more
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Hausdorff Distance with k-Nearest Neighbors
2012Hausdorff distance (HD) is an useful measurement to determine the extent to which one shape is similar to another, which is one of the most important problems in pattern recognition, computer vision and image analysis. Howeverm, HD is sensitive to outliers. Many researchers proposed modifications of HD.
Jun Wang 0002, Ying Tan 0002
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k-Nearest-Neighbor Clustering and Percolation Theory
Algorithmica, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shang-Hua Teng, Frances F. Yao
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A ?soft?K-nearest neighbor voting scheme
International Journal of Intelligent Systems, 2001Summary: The \(K\)-Nearest Neighbor (\(K\)-NN) voting scheme is widely used in problems requiring pattern recognition or classification. In this voting scheme an unknown pattern is classified according to the classifications of its \(K\) nearest neighbors.
H. B. Mitchell, P. A. Schaefer
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k-Nearest Neighbor Queues with Delayed Information
International Journal of Bifurcation and Chaos, 2022In this paper, we analyze a model called the k-nearest neighbor queue with the possibility of having delayed queue length feedback. We prove fluid limits for the stochastic queueing model and show that the fluid limit converges to a system of delay differential equations.
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Efficient reverse k-nearest neighbor estimation
Informatik - Forschung und Entwicklung, 2007The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and ...
Elke Achtert +5 more
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