[labelbias]

RBF kernel represents vectors of infinite dimension and that makes the dataset linearly separable. Increasing dimensions improves separability and going to infinity makes it always true. Representation is not infinite but is implicitly infinite.

For example when you use polynomial kernels you do not add extra dimensions to input vectors explicitly but you could use a linear kernel and add quadratic terms to the input vectors and still get the same separability.