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English(EN) Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate

新的损失函数增强了机器学习模型对离群值的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了新的损失函数和回归模型，旨在提高机器学习数据集对离群值的鲁棒性。所提出的方法根据离群值敏感性调节学习率，重点关注比现有方法（如 Huber 和 log-cosh 损失）更接近绝对误差的替代损失函数。该论文介绍了平方根损失 (SRL) 和平滑平均绝对误差 (SMAE) 作为更优的替代方案，通过在各种基准测试上的比较证明了它们的有效性，并提出了针对 GPU 优化的向量化参数更新公式的新型鲁棒线性回归模型。 AI

影响 提高了机器学习模型在存在噪声数据的实际场景中的可靠性。

排序理由 详细介绍新方法和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的损失函数增强了机器学习模型对离群值的鲁棒性

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详细介绍新方法和模型的学术论文。[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) · Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal, Geraldine Bessie Amali D, Venkataraman Muthiah-Nakarajan ·

    通过调节学习率实现对离群值鲁棒性的替代损失函数和回归模型

    arXiv:2606.22068v2 Announce Type: replace-cross Abstract: Most real-world datasets used for training supervised learning models are contaminated with noisy data and outliers leading to large prediction errors. This paper proposes a new approach for achieving robustness where the …

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