Prediction and Change Detection in Sequential Data for Interactive Applications
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 ex-pected to make proper predictions and request new human in-put when change points are detected. Motivated by the Trans-ductive 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.
Cite
Text
Zhou et al. "Prediction and Change Detection in Sequential Data for Interactive Applications." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Zhou et al. "Prediction and Change Detection in Sequential Data for Interactive Applications." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/zhou2008aaai-prediction/)BibTeX
@inproceedings{zhou2008aaai-prediction,
title = {{Prediction and Change Detection in Sequential Data for Interactive Applications}},
author = {Zhou, Jun and Cheng, Li and Bischof, Walter F.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {805-810},
url = {https://mlanthology.org/aaai/2008/zhou2008aaai-prediction/}
}