Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others.

LS-SVR as a Bayesian RBF Network.

We show theoretical similarities between the least squares support vector regression (LS-SVR) model with a radial basis functions (RBFs) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the regression weights. Although previous articles have pointed out similar expressions between those learning approaches, we explicitly and formally state the existing correspondences. We empirically demonstrate our result by performing computational experiments with standard regression benchmarks.

A stochastic variational framework for Recurrent Gaussian Processes models.

Gaussian Processes (GPs) models have been successfully applied to the problem of learning from sequential observations. In such context, the family of Recurrent Gaussian Processes (RGPs) have been recently introduced with a specifically designed structure to handle dynamical data. However, RGPs present a limitation shared by most GP approaches: they become computationally infeasible when facing very large datasets.