2020 39th International Conference of the Chilean Computer Science Society (SCCC) 2020
DOI: 10.1109/sccc51225.2020.9281231
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An exhaustive algorithm based on GPU to process a kNN query

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Cited by 4 publications
(2 citation statements)
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“…It typically uses the assumption of the data feature similarity that the data points can be found near one to another. The new data can be assigned a value based on how similarly the data match the points trained in the training set [ 91 , 92 ]. The advantages of k-NN algorithm can be summarized: Firstly, it is easy to implement and achieve high-accuracy results.…”
Section: Detection In Uwb Positioning Algorithmsmentioning
confidence: 99%
“…It typically uses the assumption of the data feature similarity that the data points can be found near one to another. The new data can be assigned a value based on how similarly the data match the points trained in the training set [ 91 , 92 ]. The advantages of k-NN algorithm can be summarized: Firstly, it is easy to implement and achieve high-accuracy results.…”
Section: Detection In Uwb Positioning Algorithmsmentioning
confidence: 99%
“…In [21] , a Brute-Force parallel algorithm to solve k -NN queries on a multi-GPU platform is presented. The proposed method is comprised of two stages, which first is based on pivots using the value of k to reduce the search space, and the second one uses a set of heaps to return the final results.…”
Section: Brute-force Techniquesmentioning
confidence: 99%