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Robust clustering around regression lines with high density regions

Robust clustering around regression lines with high density regions Andrea Cerioli · Domenico Perrotta Abstract: Robust methods are needed to fit regression lines when outliers are present. In a clustering framework, outliers can be extreme observations, high leverage points, but also data points which lie among the groups. Outliers are also of paramount importance in the analysis of international trade …

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Unlabeling data can improve classification accuracy

In this study we focus on the effects of sample limitations on partially supervised learning algorithms. We analyze the performance of these types of learning algorithms on small datasets under varying trade-offs between labeled and unlabeled samples. In contrast to the typical settings for partially supervised learning algorithms, the number of available unlabeled samples is also restricted. We utilize gene …

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ترجمه مقاله Exact analysis of performance models by the Method of Moments

Performance evaluation of distributed systems and service-oriented architectures is often based on stochastic models, such as closed queueing networks which are commonly solved by the Mean Value Analysis (MVA) algorithm. However, the MVA is unable to solve models with hundreds or thousands of users accessing services of multiple classes, a configuration that is often useful to predict the performance of …

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