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Abstract:
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The Support Vector Machine (SVM) has been widely applied for classification problems in both machine learning and statistics. Despite its popularity, it still has some drawbacks in certain situations. In particular, the SVM classifier may be sensitive to outliers in the training sample. Moreover, the number of support vectors (SVs) can be large in many applications. To circumvent these drawbacks, we propose the robust truncated-hinge-loss SVM (RSVM), which utilizes a truncated hinge loss. The RSVM is shown to be more robust to outliers and deliver more accurate classifiers using a smaller set of SVs than the standard SVM. Our theoretical results show that the RSVM is Fisher consistent, even when there is no dominating class, a scenario that is particularly challenging for multicategory classification.
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