Results 271 to 280 of about 131,772 (305)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
The k-Nearest-Neighbor Voronoi Diagram Revisited
Algorithmica, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chih-Hung Liu 0001 +2 more
openaire +2 more sources
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
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
Optimization of k nearest neighbor density estimates
IEEE Transactions on Information Theory, 1973Nonparametric 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 +1 more source
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
openaire +1 more source
The Research on an Adaptive k-Nearest Neighbors Classifier
2006 5th IEEE International Conference on Cognitive Informatics, 2006K-nearest neighbor (KNNC) classifier is the most popular non-parametric classifier. But it requires much classification time to search k nearest neighbors of an unlabelled object point, which badly affects its efficiency and performance. In this paper, an adaptive k-nearest neighbors classifier (AKNNC) is proposed.
Xiaopeng Yu 0004, Xiaogao Yu
openaire +1 more source
Classification Bias of the k-Nearest Neighbor Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984The 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
openaire +2 more sources