As far as I know, the only assumptions of support vector machines are independent and identically distributed data. I am planning to train and run a SVM on a number of variables that aren't naturally on the same scale. To meet the assumption of identically distributed data, I was planning on standardizing the variables; however, I'm not sure if I should do this for the training set and test set individually, or for the overall sample prior to creating the two sets of data.
It seems to me that it would be better to standardize the training set and test set individually, but I have no evidence to back this up and no citation to point to. Does anyone know if this is true? Also, is there a citation on this topic?