English(EN) New Bounds for Kernel Sums via Fast Spherical Embeddings

通过快速球形嵌入实现新的核和界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员为估计核均值开发了一种新的理论界限，改进了高维空间数据现有方法的性能。这种新颖的方法利用了快速球形嵌入定理，该定理在管理嵌入数据直径的同时保留了局部距离。这项进展在需要中等数据分布下的高精度的情况下具有潜在优势。 AI

影响引入了核均值估计的新理论界限，可能影响依赖核方法进行数据分析的算法。

排序理由这是一篇在 arXiv 上发表的理论计算机科学论文，详细介绍了核和的新界限。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Tal Wagner · 2026-05-05 04:00

通过快速球形嵌入为核和设置新界限

arXiv:2605.01263v1 Announce Type: cross Abstract: We study query time bounds for the fundamental problem of estimating the kernel mean $\frac1{|X|}\sum_{x\in X}\mathbf{k}(x,y)$ of a query $y$ in a finite dataset $X\subset\mathbb{R}^d$ up to a prescribed additive error $\varepsilo…