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English(EN) Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression

平滑基模型在表格回归中挑战树集成模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员重新审视了用于表格回归的切比雪夫多项式和各向异性RBF模型，开发了新的实现方法，并将其与树集成模型和Transformer进行了比较。虽然Transformer的准确性更高，但其计算需求限制了其应用。在CPU可行的模型中，平滑基模型在准确性方面与树集成模型相当，但表现出更好的泛化能力。研究建议在表格回归任务的选择过程中包含平滑基模型，尤其是在预测渐变和更严格的泛化有益的情况下。 AI

影响 平滑基模型为表格数据的树集成模型提供了一个有竞争力的替代方案，尤其是在泛化能力至关重要的情况下。

排序理由 该集群包含一篇详细介绍新模型和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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平滑基模型在表格回归中挑战树集成模型

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该集群包含一篇详细介绍新模型和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Luciano Gerber, Huw Lloyd ·

    重新审视切比雪夫多项式和各向异性径向基函数模型用于表格回归

    arXiv:2602.22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis. Their continuously differentiable prediction surfaces suit surrogate optimisa…

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