The support vector machine (SVM) has become very popular within the machine learning literature. Recently, SVM has received much attention from statisticians. It is well known that for multicategory classification problem, the commonly used multicategory SVM is based on the frequentist framework. In this paper, we develop a multi-class support vector machine under the Bayesian framework. Numerical studies were performed by EM and the Bayesian algorithm Gibbs sampler. Our results have shown that the classification accuracy of the Bayesian approach is comparable to that of frequentist approaches, while Bayesian approach also has the advantage of providing estimates of uncertainty in predictions.
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Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines
Semantic Scholar · Computer Science · 2019
Abstract
The support vector machine (SVM) has become very popular within the machine learning literature. Recently, SVM has received much attention from statisticians. It is well known that for multicategory classification problem, the commonly used multicategory SVM is based on the frequentist framework. In this paper, we develop a multi-class support vector machine under the Bayesian framework. Numerical studies were performed by EM and the Bayesian algorithm Gibbs sampler. Our results have shown that the classification accuracy of the Bayesian approach is comparable to that of frequentist approaches, while Bayesian approach also has the advantage of providing estimates of uncertainty in predictions.