I am currently working on Support Vector Regression and I've read that it is recommended to implement data rescaling, e.g. to interval $[-1;1]$, to obtain better results.
My first question is: should rescaling be applied only to features/variables, or also to the response vector?
My second question concerns rescaling and training/test sets. In http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf, page 4, rescaling is done independently on the training and test set, i.e. first we split both sets and then we rescale (while using the same method in both sets). But would it be correct to first rescale the whole data set and then split it?