Reinforced Multicategory Support Vector Machines
Yufeng Liu, Ming Yuan · Journal of Computational and Graphical Statistics · 2011
Support vector machines are one of the most popular machine learning methods for classification. Despite its great success, the SVM was originally designed for binary classification. Extensions to the multicategory case are important for general classification problems. In this article, we propose a new class of multicategory hinge loss functions, namely reinforced hinge loss functions. Both theoretical and numerical properties of the reinforced multicategory SVMs (MSVMs) are explored. The results indicate that the proposed reinforced MSVMs (RMSVMs) give competitive and stable performance when compared with existing approaches. R implementation of the proposed methods is also available online as supplemental materials.