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In this thesis a framework for finding anomalies in streaming data is proposed. The framework proposed is not necessarily applicable only to problems in anomaly detection, but could be applied to other problems as well. There are three main concepts at play in the framework: (i) Active Learning, a learning algorithm which can query a human specialist for labels of instance such that the model can
