Editing Support vector machine (section)

=== Bayesian SVM ===
In 2011 it was shown by Polson and Scott that the SVM admits a [[Bayesian probability|Bayesian]] interpretation through the technique of [[data augmentation]].<ref>{{cite journal |last1=Polson |first1=Nicholas G. |last2=Scott |first2=Steven L. |title=Data Augmentation for Support Vector Machines |journal=Bayesian Analysis |year=2011 |volume=6 |issue=1 |pages=1–23 |doi=10.1214/11-BA601 |doi-access=free }}</ref> In this approach the SVM is viewed as a [[graphical model]] (where the parameters are connected via probability distributions). This extended view allows the application of [[Bayesian probability|Bayesian]] techniques to SVMs, such as flexible feature modeling, automatic [[Hyperparameter (machine learning)|hyperparameter]] tuning, and [[Posterior predictive distribution|predictive uncertainty quantification]]. Recently, a scalable version of the Bayesian SVM was developed by [https://arxiv.org/search/stat?searchtype=author&query=Wenzel%2C+F Florian Wenzel], enabling the application of Bayesian SVMs to [[big data]].<ref>{{cite book |last1=Wenzel |first1=Florian |last2=Galy-Fajou |first2=Theo |last3=Deutsch |first3=Matthäus |last4=Kloft |first4=Marius |title=Machine Learning and Knowledge Discovery in Databases |chapter=Bayesian Nonlinear Support Vector Machines for Big Data |volume=10534 |pages=307–322 |year=2017 |arxiv=1707.05532 |bibcode=2017arXiv170705532W |doi=10.1007/978-3-319-71249-9_19 |series=Lecture Notes in Computer Science |isbn=978-3-319-71248-2 |s2cid=4018290 }}</ref> Florian Wenzel developed two different versions, a variational inference (VI) scheme for the Bayesian kernel support vector machine (SVM) and a stochastic version (SVI) for the linear Bayesian SVM.<ref>Florian Wenzel; Matthäus Deutsch; Théo Galy-Fajou; Marius Kloft; [http://approximateinference.org/accepted/WenzelEtAl2016.pdf ”Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine”]</ref>