When performing the linear SVM classification, it is often helpful to normalize the training data, for example by subtracting the mean and dividing by the standard deviation, and afterwards scale the test data with the mean and standard deviation of training data. Why this process changes dramatically the classification performance?
I think it can be made more clear through an example. Let's say you have two input vectors: X1 and X2. and let's say X1 has range(0.1 to 0.8) and X2 has range(3000 to 50000). Now your SVM classifier will be a linear boundary lying in X1-X2 plane. My claim is that the slope of linear decision boundary should not depend on the range of X1 and X2, but instead upon the distribution of points.
Now let make a prediction on the point (0.1, 4000) and (0.8, 4000). There will be hardly any difference in the value of the function, thus making SVM less accurate since it will have less sensitivity to points in the X1 direction.
SVM tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features when calculating the distance. If you rescale all features (e.g. to [0, 1]), they all have the same influence on the distance metric.