| Kurzfassung
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In One-Class classification, the classifier decides if points belong to a specific class. In this thesis, we propose an One-Class classification approach, suitable for active learning, that models for each point, a prediction range in which the model assumes the points state to be. The proposed classifier uses a Gaussian process. We use the Gaussian processes prediction range to derive a certainty measure, that considers the available labeled points for stating its certainty. We compared this approach against baseline classifiers and show the correlation between the classifier's uncertainty and misclassification ratio.
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