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English(EN) Regularized Least Squares Training of Quadratic Neural Networks with Applications to System Identification

新方法使用闭式解训练二次神经网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的最小二乘法来训练二次神经网络，并结合正则化来为优化问题的解设定下界。该方法为网络权重及其对数据误差的敏感性提供了闭式表达式，与反向传播等迭代方法相比，显著缩短了计算时间。该方法提供了权重和敏感性的解析表达式，并将优化与核范数最小化联系起来，在非线性系统辨识示例中证明了其效用。 AI

影响 为特定的神经网络架构提供了一种潜在更快、更稳定的训练方法。

排序理由 详细介绍一种新的机器学习训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法使用闭式解训练二次神经网络

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详细介绍一种新的机器学习训练方法的学术论文。[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) · Luis Rodrigues, Zachary Yetman Van Egmond, Mohammad R. Amiri Fard ·

    二次神经网络的正则化最小二乘训练及其在系统辨识中的应用

    arXiv:2609.17654v1 Announce Type: new Abstract: This paper proposes a least squares approach for the training of quadratic neural networks with regularization. The proposed methodology yields a lower bound on the solution of the training optimization problem for the case where th…

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