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Efficient Online Learning and Prediction of Users’ Desktop Actions

2019-11-15 | |  82 |   45 |   0

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 effificient many-class learning can perform well for action prediction, signififi- cantly improving over previously published results and baselines. This fifinding 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

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