Timezone: »

Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Ricardo Henao · Xin Yuan · Lawrence Carin

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

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

Author Information

Ricardo Henao (Duke University)
Xin Yuan (Duke University)
Lawrence Carin (KAUST)

More from the Same Authors