Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling

NeurIPS 2014 Ricardo HenaoXin YuanLawrence Carin

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... (read more)

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