Efficient Online Learning and Prediction of Users' Desktop Actions
Abstract
We investigate prediction of users' desktop activities in the Unix domain. The learning techniques we explore do not require explicit user teaching. We show that simple efficient many-class learning can perform well for action prediction, significantly improving over previously published results and baselines. This finding is promising for various human-computer interaction scenarios where a rich set of potentially predictive features is available, where there can be many different actions to predict, and where there can be considerable nonstationarity. Omid Madani, Hung Bui, Eric Yeh
Cite
Text
Madani et al. "Efficient Online Learning and Prediction of Users' Desktop Actions." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Madani et al. "Efficient Online Learning and Prediction of Users' Desktop Actions." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/madani2009ijcai-efficient/)BibTeX
@inproceedings{madani2009ijcai-efficient,
title = {{Efficient Online Learning and Prediction of Users' Desktop Actions}},
author = {Madani, Omid and Bui, Hung and Yeh, Eric},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {1457-1462},
url = {https://mlanthology.org/ijcai/2009/madani2009ijcai-efficient/}
}