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English(EN) When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion

新理论统一了机器学习中的泛化、验证和信息融合

作者 PulseAugur 编辑部 · [1 个来源] ·

提出了一个新的理论框架 gamma-CUBV，用于统一机器学习中的泛化、验证和信息融合。该框架通过累积量包络控制泛化差距，推广了交叉上界验证 (CUBV)，涵盖了包括 PAC-贝叶斯和异构源融合设置在内的各种风险界限。对于 K 折交叉验证，该理论引入了一个有效的折数 Keff，它考虑了折之间的依赖性，表明增加 K 并不总是能增强统计证据。该研究还将范围扩展到后验分布和加权多源融合，为异构小样本应用提供了原则性的验证标准。 AI

影响 为验证机器学习模型提供了一个统一的理论框架，尤其是在异构小样本设置中。

排序理由 详细介绍机器学习验证新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新理论统一了机器学习中的泛化、验证和信息融合

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详细介绍机器学习验证新理论框架的学术论文。[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) · JM Gorriz ·

    准确性何时成为证据？通用性、验证和信息融合的统一理论

    arXiv:2610.03465v1 Announce Type: cross Abstract: K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally informative under heterogeneous data. Cross Upper-Bound Validation (CUBV) replace…

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