English(EN) pESTO: Iterative p-greedy kernel regression with risk-driven Early STOpping

新的pESTO算法改进了带噪声数据的核回归

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员开发了一种名为pESTO的新型核回归算法，旨在更有效地处理带噪声的数据。这种迭代式p-greedy方法将近似误差与统计噪声分开，使用Stein无偏风险估计器（SURE）作为均方误差的代理。SURE准则提供了一种原则性的方法来确定最优近似节点数，平衡准确性和噪声贡献，数值实验证明了这一点。 AI

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · L. Bruni Bruno, P. Massa, E. Perracchione, M. Trombini · 2026-11-09 05:00

pESTO：具有风险驱动的早期停止的迭代p贪心核回归

arXiv:2611.08555v1 Announce Type: new Abstract: Kernel-based greedy algorithms select suitable approximation nodes by minimizing point-wise error indicators, such as the power function; in this context, they are usually referred to as p-greedy approaches. In the presence of noisy…

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pESTO：具有风险驱动的早期停止的迭代p贪心核回归

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