Prediction and Change Detection In Sequential Data for Interactive Applications

Li Cheng, Jun Zhou, Walter Bischof

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, we propose an online framework that naturally addresses these problems in a unified manner. Our empirical study with a synthetic dataset and a road tracking dataset demonstrates the efficacy of the proposed approach.

Subjects: 6. Computer-Human Interaction; 12. Machine Learning and Discovery

Submitted: Apr 14, 2008


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