Temporal Aspects in Stream Active Learning

by Daniel Kottke, Georg Krempl and Myra Spiliopoulou. Classification systems use data (consisting of instances and class labels) to learn a model that predicts the unknown class label of unseen instances. In contrast to the very fast and cheap generation of instances due to big data and connected systems in everyones life, the labeling of […]

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Probabilistic Active Learning in Data Streams

by Daniel Kottke, Georg Krempl, Myra Spiliopoulou.  In recent years, stream-based active learning has become an intensively investigated research topic. In this work, we propose a new algorithm for stream-based active learning that decides immediately whether to acquire a label (selective sampling). It uses Probabilistic Active Learning (PAL) to measure the spatial usefulness of each […]

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Optimised probabilistic active learning (OPAL)

by Georg Krempl, Daniel Kottke, Vincent Lemaire. In contrast to ever increasing volumes of automatically generated data, human annotation capacities remain limited. Thus, fast active learning approaches that allow the efficient allocation of annotation efforts gain in importance. Furthermore, cost-sensitive applications such as fraud detection pose the additional challenge of differing misclassification costs between classes. […]

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Data-Driven Spine Detection for Multi-Sequence MRI

by Daniel Kottke, Gino Gulamhussene, Klaus Tönnies. Epidemiology studies on vertebra’s shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with […]

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