Results 261 to 270 of about 104,528 (306)

K-Nearest Neighbors Hashing

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign() function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space.
Xiangyu He, Peisong Wang, Jian Cheng 0001   +2 more
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Improved k-nearest neighbor classification

Pattern Recognition, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yingquan Wu, Krassimir G. Ianakiev, Venu Govindaraju   +2 more
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Performance Analysis of Nearest Neighbor, K-Nearest Neighbor and Weighted K-Nearest Neighbor for the Classification of Alzheimer Disease

2020
Alzheimer’s disease (AD) has become a major health problem over the past few decades. AD can be defined as a neurodegenerative disorder that causes the brain cells to degenerate and die. AD is the most popular cause of dementia. AD is likely to be more observant in elderly people that are people above 65 years of age.
Olimpia Borgohain, Meghna Dasgupta, Piyush Kumar, Gitimoni Talukdar   +3 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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K-Nearest Neighbors

2023
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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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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

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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Hausdorff Distance with k-Nearest Neighbors

2012
Hausdorff 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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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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