论文标题
极端学习机器中的不确定性量化：分析发展，方差估计和置信区间
Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals
论文作者
论文摘要
不确定性量化对于评估机器学习模型的预测质量至关重要。在极端学习机器（ELM）的情况下，文献中提出的大多数方法对数据做出了强烈的假设，忽略输入权重的随机性或忽略置信区间估计的偏见贡献。本文提出了克服这些约束并提高对ELM变异性的理解的新颖估计。分析推导是在一般假设下提供的，支持不同可变性源的贡献的识别和解释。在同性恋性和异方差性下，提出，研究和数值测试提出了几项方差估计，显示了它们在复制预期方差行为方面的有效性。最后，通过采用关键方法来讨论置信区间估计的可行性，从而提高了榆树用户对某些陷阱的认识。该论文伴随着Scikit-Learn兼容的Python库，从而有效地计算了此处讨论的所有估计值。
Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literature make strong assumptions on the data, ignore the randomness of input weights or neglect the bias contribution in confidence interval estimations. This paper presents novel estimations that overcome these constraints and improve the understanding of ELM variability. Analytical derivations are provided under general assumptions, supporting the identification and the interpretation of the contribution of different variability sources. Under both homoskedasticity and heteroskedasticity, several variance estimates are proposed, investigated, and numerically tested, showing their effectiveness in replicating the expected variance behaviours. Finally, the feasibility of confidence intervals estimation is discussed by adopting a critical approach, hence raising the awareness of ELM users concerning some of their pitfalls. The paper is accompanied with a scikit-learn compatible Python library enabling efficient computation of all estimates discussed herein.
