Community-Guided Learning: Exploiting Mobile Sensor Users to Model Human Behavior

Modeling human behavior requires vast quantities of accurately labeled training data, but for ubiquitous people-aware applications such data is rarely attainable. Even researchers make mistakes when labeling data and consistent, reliable labels from low-commitment users are rare. In particular, users may give identical labels to activities with characteristically different signatures (e.g., Labeling eating at home or at a restaurant as "Dinner") or may give different labels to the same context (e.g., "Work" vs. "Office"). In this scenario, labels are unreliable but nonetheless contain valuable information for classification.

Provided by: Association for the Advancement of Artificial Intelligence Topic: Mobility Date Added: Apr 2010 Format: PDF

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