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In this correspondence, we derive an online adaptive one-class support vector machine. The machine structure is updated via growing and pruning mechanisms and the weights are updated using structural risk minimization principles underlying support vector machines. Our approach leads to very compact machines compared to other online kernel methods whose size, unless truncated, grows almost linearly with the number of observed patterns. The proposed method is online in the sense that every pattern is only presented once to the machine and there is no need to store past samples and adaptive in the sense that it can forget past input patterns and adapt to the new characteristics of the incoming data. Thus, the characterizing properties of our algorithm are compactness, adaptiveness and real-time processing capabilities, making it especially well-suited to solve online novelty detection problems. Regarding algorithm performance, we have carried out experiments in a time series segmentation problem, obtaining favorable results in both accuracy and model complexity with respect to two existing state-of-the-art methods.