Bayesian nonlinear support vector machines and discriminative factor modeling

Published

Conference Paper

A new Bayesian formulation is developed for nonlinear support vector machines (SVMs), based on a Gaussian process and with the SVM hinge loss expressed as a scaled mixture of normals. We then integrate the Bayesian SVM into a factor model, in which feature learning and nonlinear classifier design are performed jointly; almost all previous work on such discriminative feature learning has assumed a linear classifier. Inference is performed with expectation conditional maximization (ECM) and Markov Chain Monte Carlo (MCMC). An extensive set of experiments demonstrate the utility of using a nonlinear Bayesian SVM within discriminative feature learning and factor modeling, from the standpoints of accuracy and interpretability.

Duke Authors

Cited Authors

  • Henao, R; Yuan, X; Carin, L

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 2 / January

Start / End Page

  • 1754 - 1762

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus