Results 221 to 230 of about 84,282 (259)

Multiple k nearest neighbor search

World Wide Web, 2016
The 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, I-Fang Su, Chiang Lee, Pei-Chi Liu   +3 more
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Validation of nearest neighbor classifiers

IEEE Transactions on Information Theory, 2000
Summary: This correspondence presents a method to bound the out-of-sample error rate of a nearest neighbor classifier. The bound is based only on the examples that comprise the classifier. Thus all available examples can be used in the classifier; no examples need to be withheld to compute error bounds. The estimate used in the bound is an extension of
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Improvements in K-Nearest Neighbor Classification

2001
We 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, Krassimir G. Ianakiev, Venu Govindaraju   +2 more
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Classification with learning k-nearest neighbors

Proceedings of International Conference on Neural Networks (ICNN'96), 2002
The 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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Hausdorff Distance with k-Nearest Neighbors

2012
Hausdorff 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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On reverse-k-nearest-neighbor joins

GeoInformatica, 2014
A 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, Hans-Peter Kriegel, Peer Kröger, Johannes Niedermayer, Matthias Renz, Andreas Züfle   +5 more
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k-Nearest Neighbor Queues with Delayed Information

International Journal of Bifurcation and Chaos, 2022
In 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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Approximate Direct and Reverse Nearest Neighbor Queries, and the k-nearest Neighbor Graph

2009 Second International Workshop on Similarity Search and Applications, 2009
Retrieving the \emph{k-nearest neighbors} of a query object is a basic primitive in similarity searching. A related, far less explored primitive is to obtain the dataset elements which would have the query object within their own \emph{k}-nearest neighbors, known as the \emph{reverse k-nearest neighbor} query.
Karina Figueroa 0001, Rodrigo Paredes
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K-Nearest Neighbors

2013
This chapter gives an introduction to pattern recognition and machine learning via K-nearest neighbors. Nearest neighbor methods will have an important part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression.
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EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS

International Journal of Neural Systems, 2008
The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate.
Roberto Gil-Pita, Xin Yao 0001
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