$\nu$-svm parameter selection For the $\nu$-SVM (for both classification and regression cases) the $\nu \in (0;1)$ should be selected.
The LIBSVM guide suggests to use grid search for identifying the optimal value of the $C$ parameter for $C$-SVM, it also recommends to try following values $C = 2^{-5}, 2^{-3}, \dots, 2^{15}$.
So the question is, are there any recommendations for values of the $\nu$ parameter in case of $\nu$-SVMs?
 A: Rather than use a grid search, you can just optimise the hyper-parameters using standard numeric optimisation techniques (e.g. gradient descent).  If you don't have estimates of the gradients, you can use the Nelder-Mead simplex method, which doesn't require gradient information and is vastly more efficient than grid-search methods.  I would use the logit function to map the (0;1) range of $\nu$ onto $(-\infty;+\infty)$ to get an unconstrained optimisation problem.
If you really want to use grid search, then just spacing the evaluation points linearly in the range 0 - 1 should be fine.
A: well, actually there is an heuristic to get some meaningful start values, see the paper
"A geometric interpretation of v-SVM" and references therein.
The idea is the following: "v is an upper bound on the fraction of margin errors, a lower bound on the fraction of support vectors, and both quantities approach v asymptotically".
This number cannot exceed the quantity 2*lmin/l, where l is the total number of SVs and lmin is the minimum between the number of positive and negative SVs (labels +/-1).
Notice that this means that for inbalanced problems, you will have to work with lower values of v, so you will tend to overfit data.
A: Note that $\nu$-SVM and $C$-SVM are equivalent (at least for balanced problems). Performing a grid-search over $C$ should be more than sufficient.
Also, generally $C$-SVM is more efficient to solve (note e.g. for $\nu$-SVM in libSVM that the initial starting point will tend to set many non-support vectors to have bound $\alpha_i$, thus loading large parts of the kernel matrix that you may not need).
