Foundations of statistical natural language processing
Foundations of statistical natural language processing
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
Activity recognition from interactions with objects using dynamic Bayesian network
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Cross-domain activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Knowledge and Data Engineering
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Recently researchers have proposed activity recognition methods based on adapting activity knowledge obtained in previous spaces to a new space. Adapting activity knowledge allows us to quickly recognize activities in a new space using only small amounts of unlabeled data. However, adapting from dissimilar spaces not only does not help the recognition task, but might also lead to degraded recognition accuracy. We propose a method for automatically selecting the most promising source spaces among a number of available spaces. Our approach leads to a scalable and quick solution in real world, while minimizing the negative effects of adapting from dissimilar sources. To evaluate our algorithms, we tested our algorithms on eight real smart home datasets.