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An adaptive k-nearest neighbor algorithm
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our ...
Shiliang Sun, Rongqing Huang
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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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Classification with learning k-nearest neighbors
Proceedings of International Conference on Neural Networks (ICNN'96), 2002The 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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Validation of nearest neighbor classifiers
IEEE Transactions on Information Theory, 2000Summary: 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
2001We 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 +2 more
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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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Approximate Direct and Reverse Nearest Neighbor Queries, and the k-nearest Neighbor Graph
2009 Second International Workshop on Similarity Search and Applications, 2009Retrieving 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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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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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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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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