Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Incremental Learning in SwiftFile
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Predicting UNIX Command Lines: Adjusting to User Patterns
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
TaskTracer: a desktop environment to support multi-tasking knowledge workers
Proceedings of the 10th international conference on Intelligent user interfaces
Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
A hybrid learning system for recognizing user tasks from desktop activities and email messages
Proceedings of the 11th international conference on Intelligent user interfaces
Fewer clicks and less frustration: reducing the cost of reaching the right folder
Proceedings of the 11th international conference on Intelligent user interfaces
On updates that constrain the features' connections during learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Building respectful interface agents
International Journal of Human-Computer Studies
The Journal of Machine Learning Research
Operating system support for application-specific speculation
Proceedings of the sixth conference on Computer systems
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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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.