Suppose that after our computer works for an hour to fit an SVM on a large data set, we notice that x4, the feature vector for the fourth example, was recorded incorrectly (say, one of the decimal points is obviously in the wrong place). However, your co-worker notices that the pair (x4, y4) did not turn out to be a support point in the original fit. He says there is no need to re-fit the SVM on the corrected data set, because changing the value of a non-support point can't possibly change the fit. Is your co-worker correct?