Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines
International Journal of Data Science and Analysis
Volume 5, Issue 3, June 2019, Pages: 42-51
Received: Jun. 30, 2019; Accepted: Jul. 24, 2019; Published: Aug. 7, 2019
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Yeqian Liu, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA
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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.
Multivariate Classifcation, MSVM, MCMC, EM
To cite this article
Yeqian Liu, Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines, International Journal of Data Science and Analysis. Vol. 5, No. 3, 2019, pp. 42-51. doi: 10.11648/j.ijdsa.20190503.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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