Smartphone Interruptibility Using Density-Weighted Uncertainty Sampling with Reinforcement Learning

@article{Fisher2011SmartphoneIU,
  title={Smartphone Interruptibility Using Density-Weighted Uncertainty Sampling with Reinforcement Learning},
  author={Robert Fisher and Reid G. Simmons},
  journal={2011 10th International Conference on Machine Learning and Applications and Workshops},
  year={2011},
  volume={1},
  pages={436-441}
}
We present the In-Context application for smart-phones, which combines signal processing, active learning, and reinforcement learning to autonomously create a personalized model of interruptibility for incoming phone calls. We empirically evaluate the system, and show that we can obtain an average of 96.12% classification accuracy when predicting interruptibility after a week of training. In contrast to previous work, we leverage density-weighted uncertainty sampling combined with a… CONTINUE READING
Highly Cited
This paper has 36 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 25 extracted citations

Thyme: Improving Smartphone Prompt Timing Through Activity Awareness

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) • 2017
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 20 references

P

T. Kinnunen, E. Chernenko, M. Tuononen
Fr ”anti, and H. Li. Voice activity detection using mfcc features and support vector machine. In Int. Conf. on Speech and Computer (SPECOM07), Moscow, Russia, volume 2, pages 556–561. Citeseer • 2007
View 1 Excerpt

Predicting human interruptibility with sensors

ACM Trans. Comput.-Hum. Interact. • 2005
View 1 Excerpt

Similar Papers

Loading similar papers…