Results 221 to 230 of about 88,733 (260)

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
openaire   +1 more source

Efficient reverse k-nearest neighbor estimation

Informatik - Forschung und Entwicklung, 2007
The 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, Christian Böhm 0001, Peer Kröger, Peter Kunath, Alexey Pryakhin, Matthias Renz   +5 more
openaire   +2 more sources

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.
openaire   +1 more source

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
openaire   +3 more sources

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
openaire   +2 more sources

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
openaire   +3 more sources

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
openaire   +2 more sources

Algorithm for finding all k nearest neighbors

Computer-Aided Design, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Les A. Piegl, Wayne Tiller
openaire   +1 more source

The k-Nearest-Neighbor Voronoi Diagram Revisited

Algorithmica, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chih-Hung Liu 0001, Evanthia Papadopoulou, Der-Tsai Lee   +2 more
openaire   +3 more sources

Optimization of k nearest neighbor density estimates

IEEE Transactions on Information Theory, 1973
Nonparametric density estimation using the k -nearest-neighbor approach is discussed. By developing a relation between the volume and the coverage of a region, a functional form for the optimum k in terms of the sample size, the dimensionality of the observation space, and the underlying probability distribution is obtained. Within the class of density
Keinosuke Fukunaga, Larry D. Hostetler
openaire   +2 more sources