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Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross-validation (CV) or bootstrap are stochastic and thus require multiple repetitions in order to produce reliable results, which can be computationally expensive if not prohibitive. The correntropy-based Density Preserving Sampling proceduredoi:10.1109/ijcnn.2010.5596717 dblp:conf/ijcnn/BudkaG10 fatcat:bodxlgh6azgdpnfrrlkqvds4ci