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对表格数据的神经网络的测试时间本地训练

Myeonginn Kang¹, Seokho Kang²

¹Department of Industrial Engineering, Sungkyunkwan University, Jangan-gu, 16419, Suwon, Republic of Korea.

Scientific reports

|December 9, 2025

概括

本研究介绍了一种新的测试时间局部训练方法,用于表格数据上的神经网络. 它通过微调模型与最近的邻居以更好地适应本地结构来提高概括性.

科学领域:

机器学习机器学习
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

神经网络通常在全球范围内进行训练,优化整个数据集的参数.
全球培训可能会忽视本地数据结构,可能会损害稀疏地区的泛化.
现有的测试时间适应方法通常集中在视觉领域,不容易适用于表格数据.

研究的目的:

为表格数据提出一个测试时间的本地培训方法.
通过在推理过程中适应本地数据结构来增强神经网络的概括性.
解决低密度数据区域全球培训的局限性.

主要方法:

对于表格数据,引入了测试时间本地培训方法.
对于每个查询实例,从训练数据集中识别出最近的邻居.
全球训练的神经网络通过与这些邻居微调定位.

主要成果:

在表格基准数据集 (回归和分类) 上进行了实验.
拟议的方法显示了神经网络概括能力的显著提高.
当地适应改善了业绩,特别是在数据稀缺的地区.

结论:

拟议的测试时间本地训练方法有效地改善了表格数据上的神经网络概括.
将模型调整到围绕查询实例的本地结构对于性能至关重要.
这种方法提供了一种可行的解决方案,用于提高神经网络在各种数据分布中的稳定性.

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