Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities

摘要

I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilistic interpretation can provide intuitive guidelines for choosing a 'good' SVM kernel. Beyond this, it allows Bayesian methods to be used for tackling two of the outstanding challenges in SVM classification: how to tune hyperparameters-the misclassi- fication penalty C, and any parameters specifying the kernel-and how to obtain predictive class probabilities rather than the conventional deterministic class label predictions.