Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Using Pervasive Computing to Deliver Elder Care
IEEE Pervasive Computing
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
Activity Recognition Based on Semi-supervised Learning
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
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Activity recognition is an important topic in ubiquitous computing. In activity recognition, supervised learning techniques have been widely applied to learn the activity models. However, most of them can only utilize labeled samples for learning even though a large amount of unlabeled samples exist. In our previous work, we have proposed a semi-supervised learning method which can utilize both labeled and unlabeled samples for learning. As an alternative, a new learning method is proposed in this work. It makes use of the unlabeled samples to remove the noises from labeled samples, so that the learning performance is improved. Experimental results show the effectiveness of our method.