论文标题
二进制分类的软SVM回归
Soft-SVM Regression For Binary Classification
论文作者
论文摘要
二项式偏差和SVM铰链损耗函数是机器学习中使用最广泛的损失功能之一。尽管它们之间有许多相似之处，但在处理不同类型的数据时，它们也具有自己的优势。在这项工作中，我们基于使用柔软度和类别分离参数对铰链损失函数的凸松弛介绍了一个新的指数族。这个新家族（表示软SVM）使我们可以开出一个广义的线性模型，该模型有效地桥接了逻辑回归和SVM分类。该新模型是可以解释的，可以避免数据可分离性问题，通过自动通过软性参数调整数据标签可分离性，从而获得良好的拟合和预测性能。当我们比较正规逻辑，SVM和Soft-SVM回归时，通过模拟和案例研究证实了这些结果，并得出结论，所提出的模型在分类和预测错误方面的表现良好。
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.
