AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Efficient Online Learning and Prediction of Users' Desktop Actions
Omid Madani, Hung Bui, Eric Yeh

Last modified: 2009-06-26


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.


online learning, nonstationarity, many-class learning, activity prediction, user modeling

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