新的DSCA策略改进符号回归校准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

提出了一种名为Dirichlet-Sinkhorn Constant Averaging (DSCA) 的新校准策略，用于模拟符号回归。该方法旨在通过在不同的协变量分布上评估候选结构来提高进化算法中的选择压力。DSCA将优化数据划分为具有不同协变量分布的子集，在这些子集上独立校准每个候选，然后对结果参数进行平均。与传统的校准方法（如Broyden-Fletcher-Goldfarb-Shanno和Levenberg-Marquardt）相比，该方法在误设情况下能更好地提高函数恢复和准确性-复杂度权衡。 AI

影响引入了一种新颖的校准策略，可以提高符号回归模型的准确性和效率。

排序理由该集群包含一篇详细介绍符号回归新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍符号回归新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mattia Billa, Veronica Guidetti, Federica Mandreoli · 2026-10-09 04:00

Symbolic Regression 中的系数校准作为选择压力

arXiv:2610.10931v1 Announce Type: new Abstract: In memetic symbolic regression, candidate structures are compared after coefficient calibration, so the calibration protocol itself contributes to evolutionary selection. Standard centralized calibration evaluates each structure at …

本文如何被排名

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Symbolic Regression 中的系数校准作为选择压力

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