Bayesian Nonlinear Support Vector Machines for Big Data
Machine Learning
2018-03-22 v1 Machine Learning
Abstract
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.
Cite
@article{arxiv.1707.05532,
title = {Bayesian Nonlinear Support Vector Machines for Big Data},
author = {Florian Wenzel and Theo Galy-Fajou and Matthaeus Deutsch and Marius Kloft},
journal= {arXiv preprint arXiv:1707.05532},
year = {2018}
}
Comments
accepted as conference paper at ECML-PKDD 2017