English(EN) Outliers and the Hard-Margin Linear SVM in High-Dimension, Low-Sample-Size Settings

新论文分析了离群点对高维SVM的影响

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

本文研究了在一侧污染的情况下，离群点对高维低样本量设置下硬间隔线性支持向量机（SVM）的影响。研究表明，对于单侧污染，中心化Gram矩阵收敛到特定结构，从而允许对偶问题有闭式解。该分析为内点支持丢失和间隔收缩等问题提供了明确的阈值。研究还考察了双侧类别中的污染，结果表明尽管间隔减小，对称污染不会改变极限判别函数。 AI

影响为SVM的鲁棒性提供了理论见解，可能为未来的算法开发提供信息。

排序理由关于机器学习理论方面的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

本文如何被排名

Signal score: 32 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于机器学习理论方面的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yugo Nakayama · 2026-11-11 05:00

高维低样本量设置下的异常值与硬间隔线性SVM

arXiv:2611.13869v1 Announce Type: cross Abstract: We study how outliers affect the hard-margin linear support vector machine when the dimension grows while the sample sizes remain fixed. For one-sided contamination, the centered Gram matrix converges to a constant inlier block pl…

本文如何被排名

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高维低样本量设置下的异常值与硬间隔线性SVM

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