Fisher opened many new areas in Multivariate Analysis, and the one which we will consider is discriminant analysis. Several papers by Fisher and others followed from his seminal paper in 1936 where he coined the name discrimination function. Historically, his four papers on discriminant analysis during 1936-1940 connect to the contemporaneous pioneering work of Hotelling and Mahalanobis. We revisit the famous iris data which Fisher used in his 1936 paper and in particular, test the hypothesis of multivariate normality for the data which he assumed. Fisher constructed his genetic discriminant motivated by this application and we provide a deeper insight into this construction; however, this construction has not been well understood as far as we know. We also indicate how the subject has developed along with the computer revolution, noting newer methods to carry out discriminant analysis, such as kernel classifiers, classification trees, support vector machines, neural networks, and deep learning. Overall, with computational power, the whole subject of Multivariate Analysis has changed its emphasis but the impact of this Fisher's pioneering work continues as an integral part of supervised learning in Artificial Intelligence.

翻译：费舍尔在多元分析领域开辟了许多新方向，本文关注的是判别分析。自1936年他首创“判别函数”这一术语的奠基性论文以来，多篇由费舍尔及其他学者撰写的论文相继问世。从历史角度看，他在1936年至1940年间发表的四篇关于判别分析的论文，与同期霍特林和马哈拉诺比斯的开创性工作紧密相连。我们重新审视了费舍尔在1936年论文中使用的著名鸢尾花数据集，特别是检验了他假设的数据多元正态性。费舍尔受该应用启发构建了遗传判别函数，我们对此构建过程提供了更深入的见解；然而据我们所知，这一构建方式迄今尚未被充分理解。我们还指出了该领域如何随计算机革命而发展，列举了进行判别分析的新方法，例如核分类器、分类树、支持向量机、神经网络和深度学习。总体而言，借助计算能力的提升，整个多元分析领域的重心已发生转变，但费舍尔这项开创性工作的影响仍作为人工智能中监督学习的核心组成部分延续至今。