Bayesian Nonlinear Support Vector Machines for Big Data

07/18/2017

∙

We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.

READ FULL TEXT

Bayesian Nonlinear Support Vector Machines for Big Data

Qualitative Robustness of Support Vector Machines

Exploring the Entire Regularization Path for the Asymmetric Cost Linear Support Vector Machine

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data

Inferring linear and nonlinear Interaction networks using neighborhood support vector machines

Mean field variational Bayesian inference for support vector machine classification

Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

Tropical Support Vector Machines: Evaluations and Extension to Function Spaces

Bayesian Nonlinear Support Vector Machines for Big Data

Related Research

Qualitative Robustness of Support Vector Machines

Exploring the Entire Regularization Path for the Asymmetric Cost Linear Support Vector Machine

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data

Inferring linear and nonlinear Interaction networks using neighborhood support vector machines

Mean field variational Bayesian inference for support vector machine classification

Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

Tropical Support Vector Machines: Evaluations and Extension to Function Spaces