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- W3194389628 abstract "Recent workat the U.S. Army CCDC Ground Vehicle Systems Center has shown that machine learning classifiers can quickly construct high-fidelity mobility maps. Training these classifiers, on the other hand, is still a challenge, since each data instance is labeled by performing a computationally intensive, physics-based simulation. In this paper we introduce an active learning framework, based on the query-by-bagging algorithm, that substantially reduces the number of simulations needed to train a classifier. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling." @default.
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- W3194389628 date "2021-10-01" @default.
- W3194389628 modified "2023-09-23" @default.
- W3194389628 title "An Active Learning Framework for Constructing High-Fidelity Mobility Maps" @default.
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- W3194389628 doi "https://doi.org/10.1109/tvt.2021.3107338" @default.
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