关于数值不稳定性在下一代水库计算中的出现
Edmilson Roque Dos Santos1,2, Erik Bollt1,2
1Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699, USA.
下一代水库计算 (NGRC) 可以有效地预测混乱的时间序列. 通过分析特征矩阵,研究人员发现,单值分解 (SVD) 训练可以在没有规范化的情况下提高准确性,从而降低计算成本.
科学领域:
- 机器学习 机器学习
- 动态系统 动态系统
- 数字线性代数的数值线性代数.
背景情况:
- 下一代水库计算 (NGRC) 是一种具有成本效益的机器学习技术,用于预测混乱的时间序列.
- 计算效率对于扩展储库计算至关重要,需要改进培训成本降低策略.
研究的目的:
- 研究NGRC特征矩阵的数值条件及其长期动态之间的关系.
- 探索减少NGRC培训成本的方法,而不影响准确性.
主要方法:
- 利用数字线性代数和动态系统的ergodic理论的工具来分析跨超参数的特征矩阵条件.
- 评估了查莱斯基,单数值分解 (SVD) 和下-上分解算法的性能,用于解决规则化的最小平方问题.
主要成果:
- 在NGRC的特征矩阵往往是不良条件,短时间滞后,高度多项式,和有限的训练数据长度.
- 基于Singular Value Decomposition (SVD) 的培训证明了无需规范化的准确预测能力,优于其他评估的算法.
结论:
- 特性矩阵调节和NGRC的长期性能之间存在直接联系.
- 基于SVD的培训为NGRC提供了计算效率高和准确的方法,消除了规范化需求.
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