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We compare the performance of averaged regularized estimators. We show that the improvement in performance which can be achieved by averaging depends critically on the degree of regulariza-tion which is used in training the individual estimators. We compare four diierent averaging approaches: simple averaging, bagging , variance-based weighting and(More)
Fraud detection refers to the attempt to detect illegitimate usage of a communications network. Three methods to detect fraud are presented. Firstly, a feed-forward neu-ral network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, Gaussian mixture model is used to model the(More)
Fraud detection refers to the attempt to detect illegitimate usage of a communications network. Three methods to detect fraud are presented. Firstly, a feed-forward neu-ral network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, Gaussian mixture model is used to model the(More)
In data mining and in classification specifically, cost issues have been undervalued for a long time, although they are of crucial importance in real-world applications. Recently, however, cost issues have received growing attention, see for example [1,2,3]. Cost-sensitive classifiers are usually based on the assumption of constant misclassification costs(More)
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