I've got a big doubt about SVM classification task (and more in general classification task), about data normalization. Let's suppose I've a SVM trained with normalized data, and new data to classify.
1) How do I normalize new data? Please note that I don't know them when I normalized and trained my SVM.
2) Which is the best/proper normalization method? Min-max or zero-mean+variance?
A possible solution that I thought is: once new data arrives, and as we are working with SVs (that are, part of the training data), we can de-normalize the SVs, re-calculate min-Max/mean-var of the new WHOLE dataset, and normalize the new data and re-normalize the SVs. What about this?
Thanks in advance, Ivano