Soft-SVM Regression For Binary Classification. (arXiv:2205.11735v1 [stat.ML])

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 …

arxiv binary classification ml regression svm