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English(EN) How Fast Do Signatures Learn? Statistical Theory and Applications for Path Regression

新的统计理论量化了路径回归的签名学习率

作者 PulseAugur 编辑部 · [1 个来源] ·

本文介绍了基于签名的路径回归的统计理论，重点关注有限水平签名逼近路径值数据的速度。研究人员为光滑的 Itô 扩散泛函建立了 L^2 近似率，并证明了其 minimax 最优性。该研究还通过传播截断误差分析了三种统计学习程序—Signature-OLS、Signature-LASSO 和 Signature-Logistic—的一致性。在金融、能源和医学领域的应用表明，签名可以有效地表示路径值协变量，并与传统特征相比可以提高预测精度。 AI

影响 为在机器学习中使用路径签名提供了理论基础，有可能改善各个领域的时序分析和预测。

排序理由 该集群包含一篇关于使用路径签名进行路径回归的统计理论和应用的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新的统计理论量化了路径回归的签名学习率

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该集群包含一篇关于使用路径签名进行路径回归的统计理论和应用的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Blanka Horvath, Wen Su, Wu Su, Binnan Wang, Ruixun Zhang ·

    签名学习速度有多快？路径回归的统计理论与应用

    arXiv:2607.17865v1 Announce Type: cross Abstract: Many prediction and decision-making problems in operations research involve path-valued covariates -- data that evolve over time -- for which path signatures have become a canonical feature representation. Their use is justified b…

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