A binary-response regression model based on support vector machines

Abstract: The soft-margin support vector machine (SVM) is a ubiquitous tool for prediction of binary-response data. However, the SVM is characterized entirely via a numerical optimization problem, rather than a probability model, and thus does not directly generate probabilistic inferential statements as outputs. We consider a probabilistic regression model for binary-response data that is based on the optimization problem that characterizes the SVM. Under weak regularity assumptions, we prove that the maximum likelihood estimate (MLE) of our model exists, and that it is consistent and asymptotically normal. We further assess the performance of our model via simulation studies, and demonstrate its use in real data applications regarding spam detection and well water access.