Training nu-support vector regression: theory and algorithms.
We discuss the relation between epsilon-support vector regression (epsilon-SVR) and nu-support vector regression (nu-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and nu-support vector classification (nu-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of epsilon and the scaling of target values. A practical decomposition method for nu-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.