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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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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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EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS
International Journal of Neural Systems, 2008The 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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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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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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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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Hybrid k -Nearest Neighbor Classifier.
IEEE transactions on cybernetics, 2016Conventional 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 +5 more
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A Centroid k-Nearest Neighbor Method
2010K-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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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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Algorithm for finding all k nearest neighbors
Computer-Aided Design, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Les A. Piegl, Wayne Tiller
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