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Improved k-nearest neighbor classification
Pattern Recognition, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Yingquan +2 more
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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
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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
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k-Nearest Neighbor Classification
2009The k-nearest neighbor (k-NN) method is one of the data mining techniques considered to be among the top 10 techniques for data mining [237]. The k-NN method uses the well-known principle of Cicero pares cum paribus facillime congregantur (birds of a feather flock together or literally equals with equals easily associate).
Antonio Mucherino +2 more
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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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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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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
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Weighted K-Nearest Neighbor revisited
2016 23rd International Conference on Pattern Recognition (ICPR), 2016In 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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Multiple k nearest neighbor search
World Wide Web, 2016The 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 +3 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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