English(EN) Why not to use the Gaussian kernel

研究人员建议不要在机器学习中使用高斯核

作者 PulseAugur 编辑部 · [2 个来源] · 2026-08-27 11:19

最近的一篇论文认为，在回归和分类等机器学习任务中不应广泛使用高斯核。作者认为，这种核（也称为平方指数核或径向基函数核）会导致条件方差不切实际地小，从而导致预测不确定性过度自信。此外，这种平滑性会导致数值病态，在实际应用中需要引入金块项等变通方法。该论文广泛建议由于这些问题而避免使用解析核。 AI

影响建议在回归和分类任务的模型可靠性和数值稳定性方面进行潜在改进。

排序理由该集群包含一篇讨论特定机器学习核的理论局限性的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-27 11:19

为什么不使用高斯核

Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel, other names of which include squared exponential and radial basis function kernel, is one of the most popular in Gaussian process regression. We argue that the Gaussian …
arXiv stat.ML TIER_1 English(EN) · Toni Karvonen, Chris J. Oates · 2026-08-28 04:00

为什么不使用高斯核

arXiv:2608.26974v1 Announce Type: new Abstract: Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel, other names of which include squared exponential and radial basis function kernel, is one of the most popular in Gaussian…