English(EN) Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification

新方法估计标签噪声转移矩阵并提供性能保证

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种新的标签噪声转移矩阵估计方法，这是在标记不准确的数据上训练的机器学习模型的关键组成部分。这种新颖的方法利用单边选择性分类，无需复杂的类后验估计即可提供有限样本性能保证。该方法专为二元分类任务设计，并包含具有改进性能界限的算法。 AI

影响这项研究可以提高在大型、标记不准确的数据集上训练的机器学习模型的可靠性。

排序理由该条目是发表在 arXiv 上的学术论文，详细介绍了一种新的机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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 stat.ML TIER_1 English(EN) · Xabier de Juan, Santiago Mazuelas, Yilun Zhu, Clayton Scott · 2026-10-01 04:00

利用选择性分类估计标签噪声转移矩阵并提供性能保证

arXiv:2609.39829v1 Announce Type: new Abstract: Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning from noisily-labeled data has become common, making accurate estimation of the la…