Zhou, J., Cheng, L. and Bischof, W. F. (2008). Prediction and Change Detection In Sequential Data for Interactive Applications. 23rd AAAI Conference on Artificial Intelligence, Chicago, USA, July 13-17, 805-810.
We consider the problems of sequential prediction and change detection that arise often in interactive applications: A semi-automatic predictor is applied to a time-series and is expected to make proper predictions and request new human input when change points are detected. Motivated by the Transductive Support Vector Machines [Vapnik98], we propose an online framework that naturally address these problems in a unified manner. Our empirical study with a synthetic dataset and a road tracking dataset demonstrate the efficacy of the proposed approach.
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