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English(EN) Deterministic Bounds on Accuracy and Stability of Metric-Space Regression and Classification

新的几何框架量化数据集模糊性和误差界限

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文介绍了一个几何框架，用于量化由噪声和信息不足引起的数据集模糊性。该框架为回归和分类任务提供了可实现的经验误差和准确率的最佳确定性、非渐近界限，且独立于所使用的具体机器学习模型。该研究由Nina Maria Gottschling撰写，证明了输入-目标关系模糊性会导致经验误差下限以及准确率-稳定性权衡，实验显示了可实现误差和准确率的非平凡界限。 AI

影响 为理解因数据质量导致的模型局限性提供了理论基础，可能指导未来鲁棒机器学习的研究。

排序理由 学术论文发布在arXiv上，详细介绍了一个新的理论框架。[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 →

新的几何框架量化数据集模糊性和误差界限

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学术论文发布在arXiv上，详细介绍了一个新的理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nina Maria Gottschling, Jakob Gawlikowski, Michele Caprio ·

    度量空间回归与分类的精度和稳定性确定性界限

    arXiv:2610.22564v1 Announce Type: new Abstract: Data-driven approaches often face ambiguity in datasets due to sensor or target noise, insufficient information, and noise in the input data. While best-fit machine learning methods can accommodate such noise through uncertainty est…

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