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English(EN) Overfitting has a limitation: a model-independent generalization gap bound based on R\'enyi entropy

新界限将泛化差距与数据熵联系起来

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的方法来界定机器学习模型的泛化差距，这是理解过拟合的关键因素。这种新颖的方法建立了一个模型无关的泛化差距上限，仅取决于数据的 Rényi 熵。研究结果表明，如果数据量相对于数据分布的熵足够大，大型模型就能保持良好的泛化性能。该框架还解释了为什么向数据添加随机噪声会通过增加数据的 Rényi 熵来降低性能。 AI

影响 为模型泛化和过拟合提供了理论解释，可能指导未来的模型扩展和数据增强策略。

排序理由 学术论文，介绍了机器学习中泛化差距的新理论界限。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新界限将泛化差距与数据熵联系起来

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学术论文，介绍了机器学习中泛化差距的新理论界限。[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) · Atsushi Suzuki, Jing Wang ·

    过拟合存在局限性：基于 R\'enyi 熵的模型无关泛化差距界限

    arXiv:2506.00182v3 Announce Type: replace Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in understanding generalization gap, which is the impact of overfitting. Understanding general…

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