On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
IEEE Transactions on Information Technology in Biomedicine
Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Cross-people mobile-phone based activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Introducing a New Benchmarked Dataset for Activity Monitoring
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Creating and benchmarking a new dataset for physical activity monitoring
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Personalization of activity recognition has become a topic of interest recently. This paper presents a novel concept, using a set of classifiers as general model, and retraining only the weight of the classifiers with new labeled data from a previously unknown subject. Experiments with different methods based on this concept show that it is a valid approach for personalization. An important benefit of the proposed concept is its low computational cost compared to other approaches, making it also feasible for mobile applications. Moreover, more advanced classifiers (e.g. boosted decision trees) can be combined with the new concept, to achieve good performance even on complex classification tasks. Finally, a new algorithm is introduced based on the proposed concept, which outperforms existing methods, thus further increasing the performance of personalized applications.