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Real-Time Regression with Dividing Local Gaussian Processes
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models.
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems.
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