快速的二进制物流回归
Nurdan Ayse Saran1, Fatih Nar2
1Department of Computer Engineering, Cankaya University, Ankara, Türkiye.
PeerJ. Computer science
|March 10, 2025
概括
本研究引入了一种快速二进制后勤回归 (FBLR) 方法,显著加快训练时间. 这种新的方法使用Soft-Plus近似和Lf-norm规范化来开发高效的机器学习模型.
科学领域:
- 机器学习 机器学习
- 统计 统计 统计 统计
- 数字分析 数字分析
背景情况:
- 二元逻辑回归是机器学习中广泛使用的统计模型.
- 传统的培训方法可能是计算密集的,特别是对于大型数据集.
- 特征对线性和模型规范化是后勤回归中的常见挑战.
研究的目的:
- 开发一种新的数值方法,以显著提高二进制物流回归的训练效率.
- 通过矩阵向量配方实现更快的模型参数估计和规范化.
- 解决与大型数据集和对线性特征相关的计算挑战.
主要方法:
- 采用了一种新的软-加近似方法,将参数估计重新编制成矩阵-向量形式.
- 使用Lf-规范惩罚灵活规范化 (L2,L1,L0规范),包括拦截处罚选项.
- 应用单项值分解 (SVD),包括随机的SVD和带有行减小的新SVD (SVD-RR),以处理对线性和减少复杂性.
- 开发了一个快速二进制物流回归 (FBLR) 算法.
主要成果:
- 取得的训练比传统的物流回归速度快了一倍.
- 在各种合成和OpenML数据集上证明了计算效率和有效性.
- 通过使用SVD-RR.成功管理了许多行和特征的数据集.
- 为规范化和拦截处理提供了一个灵活的框架.
结论:
- 拟议的FBLR方法在训练速度和计算效率方面提供了实质性的改进.
- 新的数值方法和规范化技术为二进制物流回归提供了强大而灵活的工具.
- 该方法在各种数据集中有效,突出其在机器学习中的通用性和实际应用性.
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