Robust truncated-hinge-loss support vector machines

Abstract

Abstract. With its elegant margin theory and accurate classification perfor-mance, the Support Vector Machine (SVM) has been widely applied in both machine learning and statistics. Despite its success and popularity, it still has some drawbacks in certain situations. In particular, the SVM classifier can be very sensitive to outliers in the training sample. Moreover, the number of support vectors (SVs) can be very large in many applications. To solve these problems, [WL06] proposed a new SVM variant, the robust truncated-hinge-loss SVM (RSVM), which uses a truncated hinge loss. In this paper, we apply the operation of truncation on the multicategory hinge loss proposed by [LLW04]. We show that the proposed robust multicategory truncated-hinge-loss SVM (RMSVM) is more robust to outliers and deliver more accurate classifiers using a smaller set of SVs than the original multicategory SVM (MSVM) proposed by [LLW04]. 1