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• W4313305320 abstract "Cross-validation is one of the important tools in machine learning, which is generally used for performance evaluation. It uses different portions of the data to test and train a model on different iterations, which leads to a high computational cost. In this paper, we present a quantum version of k-fold cross-validation to choose a good parameter for the nearest neighbor classification algorithm with a threshold t, where the classification performance is estimated efficiently. With the help of amplitude amplification and estimation, the proposed quantum algorithm achieves a polynomial speedup on the number of samples over its classical counterpart." @default.
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• W4313305320 date "2023-02-01" @default.
• W4313305320 modified "2023-10-02" @default.
• W4313305320 title "Quantum <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e836 altimg=si66.svg><mml:mi>k</mml:mi></mml:math>-fold cross-validation for nearest neighbor classification algorithm" @default.
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• W4313305320 doi "https://doi.org/10.1016/j.physa.2022.128435" @default.
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