A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Handling missing values in support vector machine classifiers
Neural Networks - 2005 Special issue: IJCNN 2005
Neural Networks - 2005 Special issue: IJCNN 2005
Regression Modeling Strategies
Regression Modeling Strategies
Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
Computational Statistics & Data Analysis
Learning classifiers when the training data is not IID
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bayesian Generalized Kernel Mixed Models
The Journal of Machine Learning Research
A mixed effects least squares support vector machine model for classification of longitudinal data
Computational Statistics & Data Analysis
Variational relevance vector machines
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.03 |
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson and Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.