Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to other use cases with device trajectories of positioned data.
Full paper title: Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data
Full abstract in IEEE Xplore
Yuxin Zhao, Feng Yin, Fredrik Gunnarsson, Fredrik Hultkratz, Johan Fagerlind
Presented in July 2016 at the 19th International Conference on Information Fusion (FUSION)
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