# Mean field variational Bayesian inference for support vector machine classification

arXiv: https://arxiv.org/abs/1305.2667 · HTML: https://mlsanity.com/paper/1305.2667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2013.

## Built on by (most cited first, 1 of 1)

- [Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework](https://mlsanity.com/paper/2601.06907/) (2026)

## Abstract (arXiv)

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.