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This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can...
In this paper we consider the weighted (1-center) location problems weighted by the possibilistic clustering method. Such weighting method leads to better robustness to outliers compared with minimax location estimation. Its effectiveness was further verified by the experiments on the artificial dataset.
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