Results 211 to 220 of about 83,604 (260)

Improved k-nearest neighbor classification

Pattern Recognition, 2002
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Yingquan Wu, Krassimir G. Ianakiev, Venu Govindaraju   +2 more
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K-Nearest Neighbors

2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
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Weighted K-Nearest Neighbor revisited

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular,
BICEGO, Manuele, Loog, M.
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A fuzzy K-nearest neighbor algorithm

IEEE Transactions on Systems, Man, and Cybernetics, 1985
Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of ...
James M. Keller, Michael R. Gray, James A. Givens   +2 more
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K-Nearest Neighbors

2023
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An adaptive k-nearest neighbor algorithm

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
An 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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Multiple k nearest neighbor search

World Wide Web, 2016
The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas ...
Yu-Chi Chung, I-Fang Su, Chiang Lee, Pei-Chi Liu   +3 more
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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

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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k-Nearest Neighbor Queues with Delayed Information

International Journal of Bifurcation and Chaos, 2022
In this paper, we analyze a model called the k-nearest neighbor queue with the possibility of having delayed queue length feedback. We prove fluid limits for the stochastic queueing model and show that the fluid limit converges to a system of delay differential equations.
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