Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Hinge loss,Pattern recognition,Markov chain Monte Carlo,Computer science,Support vector machine,Gaussian process,Artificial intelligence,Linear classifier,Discriminative model,Machine learning,Feature learning,Bayesian probability |
DocType | Volume | ISSN |
Conference | 27 | 1049-5258 |
Citations | PageRank | References |
9 | 0.53 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Henao | 1 | 286 | 23.85 |
| Xin Yuan | 2 | 383 | 27.60 |
| L. Carin | 3 | 4603 | 339.36 |