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English(EN) The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity

新研究探讨回归中的插值与聚合

作者 PulseAugur 编辑部 · [1 个来源] ·

这篇研究论文深入探讨了回归模型中插值和聚合的理论方面。作者引入了 $\gamma$-图维度作为理解各种聚合技术可学性的关键因素。他们证明，三个插值假设的简单基于中位数的聚合可以达到最优性能，超越了传统的适当学习方法。该论文还指出，某些假设类别只能通过无限聚合或非插值规则来学习，这表明了有限插值聚合的局限性。 AI

排序理由 这是一篇发表在arXiv上的理论研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新研究探讨回归中的插值与聚合

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这是一篇发表在arXiv上的理论研究论文。[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) · Mikael M{\o}ller H{\o}gsgaard, Kasper Green Larsen, Liang-Yu Zou ·

    插值与聚合在回归中的相互作用：最优样本复杂度

    arXiv:2605.29819v1 Announce Type: new Abstract: This work investigates theoretically the interplay between interpolation and aggregation in regression. We establish that the $\gamma$-graph dimension characterizes learnability for a broad class of natural aggregation procedures. F…

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