I know the basics about SVM and SVR, but still I don’t get how the problem of finding a hyperplane which maximizes the margin fits into SVR.
Second, I read something about $epsilon$ used as margin of tolerance in SVR. What does it mean?
Third, is there any difference between decision function parameters used in SVM and SVR?