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English(EN) Hybrid least squares for learning functions from highly noisy data

新的混合最小二乘法可处理嘈杂数据以进行函数逼近

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的混合最小二乘法，旨在提高从包含大量噪声的数据中进行函数逼近的准确性。该方法结合了Christoffel采样和最优实验设计，以提高计算效率并降低样本复杂度，尤其是在高噪声场景下。该方法已扩展到处理具有凸性约束的设置，并在理论和数值结果方面显示出前景，包括在计算金融领域的应用。 AI

排序理由 该集群包含一篇详细介绍新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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新的混合最小二乘法可处理嘈杂数据以进行函数逼近

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该集群包含一篇详细介绍新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ben Adcock, Bernhard Hientzsch, Akil Narayan, Yiming Xu ·

    从高度嘈杂数据中学习函数的混合最小二乘法

    arXiv:2507.02215v2 Announce Type: replace-cross Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods that are effective in the small-noise regi…

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