Eva Weigl

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Active learning facilitates the training of classifiers by selectively querying the user in order to gain insights on unlabeled data samples. Until recently, the user had limited abilities to interact with an active learning system: A sub-selection was presented by the system and every sample within had to be annotated. We propose an alternative and(More)
Classification of detected events is a central component in state-of-the-art surface inspection systems that still relies on manual parametrization. While machine-learned classifiers promise supreme accuracy, their reliability depends on complete and correct annotation of an extensive training database, leaving the risk of unpredictable behavior in changing(More)
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