Choosing ν in support vector regression with different noise models-theory and experiments

In support vector (SV) regression, a parameter ν controls the number of support vectors and the number of points that come to lie outside of the so-called ε-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression