Results 1 to 10 of about 114,723 (161)
An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance [PDF]
Sensors, 2019 WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms.
Boyuan Wang +4 more
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Euclidean Distance Degree and Mixed Volume [PDF]
Foundations of Computational Mathematics, 2021 AbstractWe initiate a study of the Euclidean distance degree in the context of sparse polynomials. Specifically, we consider a hypersurface $$f=0$$ f = 0 defined by a polynomial f that is general given its support, such that the support ...
Breiding, P., Sottile, F., Woodcock, J.
exaly +4 more sources
Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster
Jurnal Teknologi dan Sistem Komputer, 2021 K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset.
Agil Aditya +2 more
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Barekeng, 2023 An intuitionistic fuzzy set (IFS) can be helpful in decision-making as a concept to describe uncertainty. This study proposes the application of IFS in determining research topics for students of the mathematics education study program using the ...
Sutrisno Sutrisno +2 more
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An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance
Entropy, 2023 The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data.
Congcong Feng +4 more
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Penalized Euclidean distance regression [PDF]
Stat, 2018 A method is introduced for variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The methodology involves minimizing a penalized Euclidean distance, where the penalty is the geometric mean of the ℓ1 and ℓ2 norms of regression coefficients.
Daniel Vasiliu +2 more
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Euclidean Distance Geometry and Applications [PDF]
SIAM Review, 2014 Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances.
Leo Liberti +3 more
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Indonesian Journal of Statistics and Its Applications, 2021 There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance).
Said Al Afghani +1 more
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GENETIC DIVERGENCE ANALYSIS AMONG 56 BREAD WHEAT GENOTYPES [PDF]
Journal of Plant Production, 2005 Genetic diversity analysis could be used to identify those genotypes, which are useful not only for sampling in subsequent studies but also for parental selection in breeding programs.
A. Khedr, A. El-Hag, A. Menshawy,
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Kinetic Euclidean Distance Matrices [PDF]
IEEE Transactions on Signal Processing, 2020 Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize points from a snapshot of distances.
Puoya Tabaghi +2 more
openaire +2 more sources