English(EN) Machine Learning under Imperfect Data: Challenges and Methods

新调查论文概述了不完美数据的机器学习方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一篇新发表在arXiv上的调查论文详细介绍了在训练和测试数据不完美的情况下进行机器学习的方法。该论文将这些不完美之处归类为四种共享机制：信息丢失、经验风险偏差、模糊监督和不稳定的表示。然后，它回顾了解决诸如缺失数据、类别不平衡、弱监督和域偏移等问题的现有技术，并以证据感知学习和保留不确定性预测的未来研究方向作为总结。 AI

影响为提高机器学习模型在现实世界不完美数据场景下的鲁棒性提供了结构化的技术概述。

排序理由该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Masoumeh Zareapoor · 2026-09-15 04:00

不完美数据下的机器学习：挑战与方法

arXiv:2609.13914v1 Announce Type: new Abstract: Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn from compatible distributions. In practice, one or more of these conditions is o…

本文如何被排名

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不完美数据下的机器学习：挑战与方法

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