Prediction and change detection in sequential data for interactive applications

Authors:
Jun Zhou;Li Cheng;Walter F. Bischof
Affiliations:
National ICT Australia, Canberra, ACT, Australia;National ICT Australia, Canberra, ACT, Australia;Department of Computing Science, University of Alberta, Canada, Edmonton, Alberta
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Year:
2008

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Abstract

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 (Vapnik 1998), 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.