Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine

We develop a variational inference (VI) scheme for the recently proposed Bayesian kernel support vector machine (SVM) and a stochastic version (SVI) for the linear Bayesian SVM. We compute the SVM’s posterior, paving the way to apply attractive Bayesian techniques, as we exemplify in our experiments by means of automated model selection.

[1]  David M. Blei,et al.  A Variational Analysis of Stochastic Gradient Algorithms , 2016, ICML.

[2]  Shinichi Nakajima,et al.  Separating Sparse Signals from Correlated Noise in Binary Classification , 2016, CFA@UAI.

[3]  Xin Yuan,et al.  Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling , 2014, NIPS.

[4]  Bo Zhang,et al.  Max-Margin Infinite Hidden Markov Models , 2014, ICML.

[5]  Ning Chen,et al.  Gibbs max-margin topic models with data augmentation , 2013, J. Mach. Learn. Res..

[6]  John T. Ormerod,et al.  Mean field variational Bayesian inference for support vector machine classification , 2013, Comput. Stat. Data Anal..

[7]  Neil D. Lawrence,et al.  Gaussian Processes for Big Data , 2013, UAI.

[8]  Chong Wang,et al.  An Adaptive Learning Rate for Stochastic Variational Inference , 2013, ICML.

[9]  Bo Zhang,et al.  Fast Max-Margin Matrix Factorization with Data Augmentation , 2013, ICML.

[10]  Chong Wang,et al.  Stochastic variational inference , 2012, J. Mach. Learn. Res..

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Nicholas G. Polson,et al.  Data augmentation for support vector machines , 2011 .

[13]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[14]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[17]  J. S. Maritz,et al.  Empirical Bayes Methods with Applications , 1989 .