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Hierarchical domain adaptation for SEMG signal classification across multiple subjects., , and . EMBC, page 7853-7856. IEEE, (2011)Multi-source domain adaptation and its application to early detection of fatigue., , , , and . KDD, page 717-725. ACM, (2011)Transfer Learning Framework for Early Detection of Fatigue Using Non-invasive Surface Electromyogram Signals (SEMG)., , and . AAAI Spring Symposium: Computational Physiology, AAAI, (2011)Batch Mode Active Sampling Based on Marginal Probability Distribution Matching., , , , , and . ACM Trans. Knowl. Discov. Data, 7 (3): 13:1-13:25 (2013)A Novel Approach to Hardware Architecture Design and Advanced Optimization Techniques for Time Critical Applications., and . EUC (1), page 9-15. IEEE Computer Society, (2008)Topology Preserving Domain Adaptation for Addressing Subject Based Variability in SEMG Signal., , and . AAAI Spring Symposium: Computational Physiology, AAAI, (2011)Batch mode active sampling based on marginal probability distribution matching., , , , , and . KDD, page 741-749. ACM, (2012)Processing body sensor data streams for continuous physiological monitoring., , and . Multimedia Information Retrieval, page 479-486. ACM, (2010)Joint Transfer and Batch-mode Active Learning., , , , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 253-261. JMLR.org, (2013)An Efficient DSP Implementation of Real-Time Stationary Vehicle Detection by Smart Camera at Outdoor Conditions., , , , , and . ICIP, page 3273-3276. IEEE, (2006)