I like both Pablo and Marc answers. One additional point:
In the paper cited by Marc there is written (section 4)
"The motivation of $\nu$-SVR is that it may not be easy to decide the parameter
$\epsilon$. Hence, here we are interested in the possible range of $\epsilon$. As expected, results show that $\epsilon$ is related to the target values $y$.
As the effective range of $\epsilon$ is affected by the target values $y$, a way to solve this difficulty for $\epsilon$-SVM is by scaling the target values before training the data. For example, if all target values are scaled to $[-1,+1]$, then the effective range of $\epsilon$ will be $[0, 1]$, the same as that of $\nu$. Then it may be easier to choose $\epsilon$."
That brings me to think that it should be easier to scale your target variables and use $\epsilon$-SVR, than trying to decide whether to use $\epsilon -$ or $\nu -$ SVR.
What do you think?