Robust Support Vector Machines For Implicit Outlier Removal

TL;DRAbstract

The support vector machine is a machine learning algorithm which has been successfully applied to solve classification problems since its introduction in the early 1990s. It is based on the work of Vladimir Vapnik on Statistical Learning Theory and is theoretically well founded. Following the discriminative approach, the SVM yields a classifier which separates two classes by a hyperplane. The training instances are classified according to the sign of their distance to the hyperplane. This hyperplane is defined by a small number of training instances such that the distance of the training instances of both classes to the hyperplane is maximized and the misclassification error is minimized. Hence the support vector machine belongs to the family of maximum margin classifiers. Since the support vector machine does not estimate the underlying class conditional distribution of the training instances, but instead uses them directly to construct the classifier, it is important that the trainin

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