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Poster
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
in Poster Session »

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

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Author Information

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

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