Results 271 to 280 of about 592,656 (330)

A novel ensemble method for k-nearest neighbor

Pattern Recognition, 2019
In this paper, to address the issue that ensembling k-nearest neighbor (kNN) classifiers with resampling approaches cannot generate component classifiers with a large diversity, we consider ensembling kNN through a multimodal perturbation-based method ...
Youqiang Zhang, Guo Cao, Bisheng Wang, Xuesong Li   +3 more
semanticscholar   +1 more source

Experimental Analysis on Weight ${K}$ -Nearest Neighbor Indoor Fingerprint Positioning

IEEE Internet of Things Journal, 2019
Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted ${K}$ -nearest neighbor (WKNN) fingerprint which positions a user based on ${K}$ nearest reference points measured beforehand.
Jiusong Hu, Dawei Liu, Zhi Yan, Hongli Liu   +3 more
semanticscholar   +1 more source

A novel version of k nearest neighbor: Dependent nearest neighbor

Applied Soft Computing, 2017
k nearest neighbor (kNN) is one of the basic processes behind various machine learning methods In kNN, the relation of a query to a neighboring sample is basically measured by a similarity metric, such as Euclidean distance. This process starts with mapping the training dataset onto a one-dimensional distance space based on the calculated similarities,
Ertuğrul, Ömer Faruk, Tağluk, Mehmet Emin   +1 more
openaire   +2 more sources

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.
openaire   +2 more sources

Locality constrained representation-based K-nearest neighbor classification

Knowledge-Based Systems, 2019
K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern recognition. However, the KNN-based classification performance is severely affected by the sensitivity of the neighborhood size k and the simple majority voting in the ...
Jianping Gou, Wenmo Qiu, Zhang Yi, Xiang-jun Shen, Yongzhao Zhan, Weihua Ou   +5 more
semanticscholar   +1 more source

K-Nearest Neighbors

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

K-Nearest Neighbor Finding Using MaxNearestDist

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depth- first or a best-first algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on hierarchical clustering.
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

A Local Mean Representation-based K-Nearest Neighbor Classifier

ACM Transactions on Intelligent Systems and Technology, 2019
K-nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition.
Jianping Gou, Wenmo Qiu, Zhang Yi, Yong Xu, Qi-rong Mao, Yongzhao Zhan   +5 more
semanticscholar   +1 more source