Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

支持向量机（SVM）在情感科学中正越来越多地用作数据驱动的分类方法和特征约简技术。传统的统计方法通常会比较所选变量的组平均值，而SVM使用预测算法来学习可最佳区分组的多元模式。在这篇综述中，我们提供了一个框架，用于理解基于SVM的分析方法，并总结了开创性研究的发现，这些开创性研究使用SVM对情感和情感障碍的行为和神经研究进行分类或减少数据。最后，我们讨论了SVM在情感科学的未来研究中的有希望的方向和潜在的应用。