VC dimension for Gaussian Process Regression In neural networks, the VC dimension $d_{VC}$ equals approximately the number of parameters (weights) of the network. The rule of thump for good generalization is then $N \geq 10 d_{VC} \approx 10 * (\text{number of weigts})$.
What is the VC dimension for Gaussian Process Regression ?
My domain is $X = \mathbb{R}^{25}$, meaning I have 25 features, and I want to determine the number of samples $N$ I must have to archive good generalization.
The expressiveness of the gaussian process grows with a number of training points. So, the vapnik-chervonenkis dimension in fact is infinite (pretty much the same way it's infinite for k nearest neighbors) and unfortunately your rule of thumb is not applicable here.
You should probably rely on train/validation split to estimate the generalization. From my experience, GP generalizes much better than neural nets, but the exact required train size depends on data distribution complexity itself.