新的摊销框架改进了核密度估计的带宽选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-24 04:00

研究人员开发了一种新颖的摊销框架，用于学习核密度估计中的带宽选择。该方法在密度估计任务的分布上优化对数得分，即使在异构数据下也能实现稳定的学习。实验表明，与Silverman规则和最小二乘交叉验证等传统方法相比，这种摊销选择器表现显著更优，尤其是在样本量小或多样化的情况下。 AI

影响这项研究可能带来更准确的概率密度估计，惠及那些依赖于将有限样本转换为连续概率密度的应用。

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arXiv cs.LG TIER_1 English(EN) · Junyi Liang, Hailiang Du · 2026-08-24 04:00

对数得分下核密度估计的摊销带宽学习

arXiv:2608.20445v1 Announce Type: new Abstract: Kernel density estimation converts finite samples into probability densities, but its performance depends critically on bandwidth selection. Classical selectors prescribe the sample-to-bandwidth rule analytically or asymptotically, …