Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling

@inproceedings{Henao2014BayesianNS,
  title={Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling},
  author={Ricardo Henao and Xin Yuan and Lawrence Carin},
  booktitle={NIPS},
  year={2014}
}
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… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS
13 Citations
24 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 24 references

Scale mixtures of normal distributions. JRSSB

  • D. F. Andrews, C. L. Mallows
  • 1974
Highly Influential
3 Excerpts

Nonparametric maxmargin matrix factorization for collaborative prediction

  • J. Zhu, B. Zhang
  • NIPS
  • 2012

Maximum likelihood estimation via the ECM algorithm : A general framework

  • D. B. Rubin
  • NIPS
  • 2010

Scale mixtures of normal distributions

  • C. L. Mallows
  • Bayesian Statistics
  • 2010

Similar Papers

Loading similar papers…