Efficient Online Learning and Prediction of Users' Desktop Actions
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1457-1461.
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
@inproceedings{ijcai2009p244,
title = {Efficient Online Learning and Prediction of Users' Desktop Actions},
author = {Madani, Omid and Bui, Hung Hai and Yeh, Eric},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1457--1461},
year = {2009},
url = {https://ijcai.org/proceedings/2009/244},
}
TY - CPAPER TI - Efficient Online Learning and Prediction of Users' Desktop Actions AU - Madani, Omid AU - Bui, Hung Hai AU - Yeh, Eric T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1457 EP - 1461 UR - https://ijcai.org/proceedings/2009/244 ER -Copied
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