基于不可见特征的分类，从而发现结核疫苗对COVID-19的影响
Classification based on invisible features and thereby finding the effect of tuberculosis vaccine on COVID-19
摘要：
对于集群数据，具有logcosh损失函数的人工神经网络学习更大的集群，而不是两者的平均值。更重要的是，当用于集值函数的回归时，ANN将学习接近其中一个选择的值，换句话说，它以高精度学习集值函数的一个分支。本文提出了一种利用具有logcosh损失的人工神经网络在参数-结果样本集中寻找集值映射的分支并根据这些分支对样本进行分类的方法。该方法不仅可以根据这些分支对数据进行分类，而且可以对大多数聚类进行准确的预测。该方法成功地分类的数据的基础上不可见的功能。成功建立了一个神经网络，从多个输入变量中预测德国各地区冠状病毒的总病例数、对数总病例数、死亡人数、活跃病例数和其他相关数据。据推测，结核病疫苗可提供针对病毒的保护，并且由于东德在统一前接种了疫苗，因此试图通过将疫苗信息视为不可见的特征来对东德和西德地区进行分类。
英文摘要：
In the case of clustered data, an artificial neural network with logcosh loss function learns the bigger cluster rather than the mean of the two. Even more so, the ANN when used for regression of a set-valued function, will learn a value close to one of the choices, in other words, it learns one branch of the set-valued function with high accuracy. This work suggests a method that uses artificial neural networks with logcosh loss to find the branches of set-valued mappings in parameter-outcome sample sets and classifies the samples according to those branches. The method not only classifies the data based on these branches but also provides an accurate prediction for the majority cluster. The method successfully classifies the data based on an invisible feature. A neural network was successfully established to predict the total number of cases, the logarithmic total number of cases, deaths, active cases and other relevant data of the coronavirus for each German district from a number of input variables. As it has been speculated that the Tuberculosis vaccine provides protection against the virus and since East Germany was vaccinated before reunification, an attempt was made to classify the Eastern and Western German districts by considering the vaccine information as an invisible feature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
