Abstract
The past several years have seen a strong push toward using Deep Learning systems–Neural Networks with multiple hidden layers. Deep Learning is now used in many machine learning applications and provides leading performance on numerous benchmark tasks. However, this increase in performance requires very large datasets for training. From a practitioner prospective, the model that performs best in benchmark tasks may be too data intensive to be adapted to practical application. We describe a behavior recognition problem that was solved using a sequence-based Deep Learning system and then reimplemented using a more knowledge-driven sequence matching approach due to data constraints. We contrast the two approaches and the data required to achieve sufficient performance and flexibility.
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Acknowledgements
The authors thank Robert “Norb” Timpko, Victor Hung, and Chris Ballinger for contributions to concepts and implementations reported here. We also thank Dr. Heather Priest and Ms. Jennifer Pagan for contributions to problem definitions and solution context that informed our approach. Opinions expressed here are not necessarily those of the Department of Defense or the sponsor of this effort, the Naval Air Warfare Center Training Systems Division (NAWCTSD). This work was funded under contract no. N68335-17-C-0574.
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Haley, J., Wray, R., Bridgman, R., Brehob, A. (2021). Reducing the Data Cost of Machine Learning with AI: A Case Study. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_26
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DOI: https://doi.org/10.1007/978-3-030-70296-0_26
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