Deep Convolutional Support Vector Machines for Human Activity Recognition

Author:

Huynh Phuoc-Hai,Nguyen Van Hoa

Publisher

Springer International Publishing

Reference25 articles.

1. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphone-based activity recognition using fixed-point arithmetic. J. Univers. Comput. Sci. 19(9), 1295–1314 (2013)

2. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., et al.: A public domain dataset for human activity recognition using smartphones. In: Esann, vol. 3, p. 3 (2013)

3. Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: 23th International Conference on Architecture of Computing Systems 2010, pp. 1–10. VDE (2010)

4. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)

5. Chen, T., He, T.: Xgboost: extreme gradient boosting (2021)

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