Results 271 to 280 of about 104,528 (306)

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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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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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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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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k-Nearest-Neighbor Clustering and Percolation Theory

Algorithmica, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shang-Hua Teng, Frances F. Yao
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Hybrid k -Nearest Neighbor Classifier.

IEEE transactions on cybernetics, 2016
Conventional k -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches.
Zhiwen Yu 0002, Hantao Chen, Jiming Liu 0001, Jane You, Hareton Leung, Guoqiang Han 0002   +5 more
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A ?soft?K-nearest neighbor voting scheme

International Journal of Intelligent Systems, 2001
Summary: 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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A Centroid k-Nearest Neighbor Method

2010
K-nearest neighbor method (KNN) is a very useful and easy-implementing method for real applications. The query point is estimated by its K nearest neighbors. However, this kind of prediction simply uses the label information of its neighbors without considering their space distributions.
Qingjiu Zhang, Shiliang Sun
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Classification Bias of the k-Nearest Neighbor Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984
The k-nearest neighbor classifier has been used extensively in pattern analysis applications. This classifier can, however, have substantial bias when there is little class separation and the sample sizes are unequal. This classification bias is examined for the two-class situation and formulas presented that allows selection of values of k that yields
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