Activity recognition with the aid of unlabeled samples
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Boosted multi-class semi-supervised learning for human action recognition
Pattern Recognition
Training pool selection for semi-supervised learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Since labeling samples requires human's efforts, most existing research in activity recognition focus on refining learning techniques to utilize the costly labeled samples as effectively as possible. However, few of them consider using the costless unlabeled samples to boost learning performance. In this work, we propose a novel semi-supervised learning algorithm named En-Co-training to make use of the unlabeled samples. Our algorithm extends the cotraining paradigm by using ensemble method. Experimental results show that En-Co-training is able to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.