The gesture recognition toolkit

Journal of Machine Learning Research

Volumn 15, Issue , 2014, Pages 3483-3487

  Gillian, Nicholas ^a   Paradiso, Joseph A ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

C++; Classification; Clustering; Feature extraction; Gesture recognition; Gesture spotting; Machine learning; Open source; Regression; Signal processing

Indexed keywords

ARTIFICIAL INTELLIGENCE; C++ (PROGRAMMING LANGUAGE); CLASSIFICATION (OF INFORMATION); DESIGN; EXTRACTION; FEATURE EXTRACTION; INTERACTIVE COMPUTER SYSTEMS; LEARNING SYSTEMS; REAL TIME SYSTEMS; SIGNAL PROCESSING;

C; CLUSTERING; GESTURE SPOTTING; OPEN SOURCES; REGRESSION;

GESTURE RECOGNITION;

EID: 84919767023     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. T. Abeel, Y. Van de Peer, and Y. Saeys. Java-ML: A machine learning library. Journal of Machine Learning Research, 10:931-934, 2009.
2. C.C. Chang and C.J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011.
3. M. Gashler. Waffles: A machine learning toolkit. Journal of Machine Learning Research, 12:2383-2387, 2011.
4. N. Gillian, R. B. Knapp, and S. O'Modhrain. An adaptive classification algorithm semiotic musical gestures. In Proceeding of the 8th Sound and Music Computing Conference, 2011a.
5. N. Gillian, R. B. Knapp, and S. O'Modhrain. Recognition of multivariate temporal musical gestures using n-dimensional dynamic time warping. In Proceedings of the 2011 International Conference on New Interfaces for Musical Expression, 2011b.
6. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
7. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1):10-18, 2009.
8. E. L. Hutchins, J. D. Hollan, and D. A. Norman. Direct manipulation interfaces. Human-Computer Interaction, 1(4):311-338, 1985.
9. H. Junker, O. Amft, P. Lukowicz, and G. Tröster. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6):2010-2024, 2008.
10. D.E. King. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10:1755-1758, 2009.
11. D. Mellis, M. Banzi, D. Cuartielles, and T. Igoe. Arduino: An open electronics prototyping platform. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2007.
12. K. Patel, N. Bancroft, S. M. Drucker, J. Fogarty, A. J. Ko, and J. Landay. Gestalt: integrated support for implementation and analysis in machine learning. In Proceedings of the 23rd Annual ACM symposium on User Interface Software and Technology, 2010.
13. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
14. S. Sonnenburg, G. Rätsch, S. Henschel, C. Widmer, J. Behr, A. Zien, F. Bona, A. Binder, C. Gehl, and V. Franc. The SHOGUN machine learning toolbox. Journal of Machine Learning Research, 11:1799-1802, 2010.