Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Recently, polynomial graph filter learning (PGFL) has demonstrated promising performance for modeling graph signals in Graph Neural Networks (GNNs) on both homophilic and heterophilic graphs, owning ...

This paper introduces a novel kernel-based formulation for the efficient uncertainty quantification of integrated circuits. The method combines the polynomial chaos expansion (PCE) and Gaussian ...

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