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Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
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基于光学偏差和机器学习的快速准确的视力敏度预测.

A Sierra1, I Baoud-Ould-Haddi2, S Fernández-Núñez3

  • 1Departamento de Óptica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Madrid, Spain. aguesier@ucm.es.

Scientific reports
|December 19, 2025
PubMed
概括

我们开发了机器学习模型来预测视觉敏度 (VA). LSBoost回归树实现了最高的准确性,而XGBoost提供了更快的计算,使它们适合视觉补偿设计.

关键词:
住宿的范围范围范围.神经网络的神经网络的神经网络回归树是一种回归树.视力敏度 (VA) 是指视力敏度 (VA) 是指视力敏度.在XGBoost中使用.泽尼克系数的使用方法

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