标题： 软间隔支持向量机回归用于二分类 显示英文标题

标题： Soft-SVM Regression For Binary Classification

摘要： 二项偏差和SVM合页损失函数是机器学习中使用最广泛的损失函数之一。 虽然它们有许多相似之处，但在处理不同类型的数据时也有各自的优势。 在本工作中，我们引入了一种基于合页损失函数凸松弛的新指数族，该函数使用了软性参数和类别分离参数。 这个新的族被称为Soft-SVM，使我们能够指定一个广义线性模型，有效地在逻辑回归和SVM分类之间建立桥梁。 这个新模型具有可解释性，并避免了数据可分性问题，通过软性参数自动调整数据标签的可分性，从而获得良好的拟合和预测性能。 这些结果通过模拟和案例研究得到实证确认，在比较正则化逻辑回归、SVM和Soft-SVM回归时得出结论，所提出的模型在分类和预测误差方面表现良好。 显示英文摘要

摘要： The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this work, we introduce a new exponential family based on a convex relaxation of the hinge loss function using softness and class-separation parameters. This new family, denoted Soft-SVM, allows us to prescribe a generalized linear model that effectively bridges between logistic regression and SVM classification. This new model is interpretable and avoids data separability issues, attaining good fitting and predictive performance by automatically adjusting for data label separability via the softness parameter. These results are confirmed empirically through simulations and case studies as we compare regularized logistic, SVM, and Soft-SVM regressions and conclude that the proposed model performs well in terms of both classification and prediction errors.