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The aim of this paper is to analyse the phenomenon of accuracy degradation in the samples given as input to SVM classification algorithms. In particular, the effect of accuracy degradation on the performance of the learnt classifiers is investigated and compared, if possible, with theoretical results. The study shows how a family of SVM classification algorithms enhanced in order to deal with quality measures on the available data handles accuracy degradation better than the classical SVM approaches to classification.