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English(EN) Non-asymptotic estimates of the minimal risk in statistical learning

新的统计学习界限提供高效风险检测

作者 PulseAugur 编辑部 · [1 个来源] ·

本文在统计学习的经验风险原理（ERP）中引入了新的误差概率集中不等式。这些不等式为最小风险建立了非渐近高置信度的下界和上界，将经验风险函数的传统有界性条件放宽到高斯或指数可积性。下界的置信度独立于训练参数和输入维度，能够有效检测学习机器的缺陷。当样本量显著超过Orlicz度量下参数集的盒维数时，上界的置信度很高。 AI

影响 为更鲁棒的统计学习模型提供了理论基础，可能提高AI系统的可靠性。

排序理由 学术论文发表在arXiv上，详细介绍了统计学习的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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

新的统计学习界限提供高效风险检测

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学术论文发表在arXiv上，详细介绍了统计学习的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sen Yang ·

    统计学习中最小风险的非渐近估计

    In this paper we prove some concentration inequalities for two types of error probabilities in the Empirical Risk Principle (ERP) in statistical learning, which provide a lower bound and an upper bound for the minimal risk (in terms of the minimal empirical risk) with non-asympto…

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