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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.
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