Which search range for determining SVM optimal C and gamma parameters? I am using SVM for classification and I am trying to determine the optimal parameters for linear and RBF kernels. For the linear kernel I use cross-validated parameter selection to determine C and for the RBF kernel I use grid search to determine C and gamma.
I have 20 (numeric) features and 70 training examples that should be classified into 7 classes.  
Which search range should I use for determining the optimal values for the C and gamma parameters?
 A: Check out A practical guide to SVM Classification for some pointers, particularly page 5. 

We recommend a "grid-search" on $C$ and $\gamma$ using cross-validation. Various pairs of $(C,\gamma)$ values are tried and the one with the best cross-validation accuracy is
  picked. We found that trying exponentially growing sequences of $C$ and $\gamma$ is a
  practical method to identify good parameters (for example, $C = 2^{-5},2^{-3},\ldots,2^{15};\gamma = 2^{-15},2^{-13},\ldots,2^{3}$).

Remember to normalize your data first and if you can, gather more data because from the looks of it, your problem might be heavily underdetermined.
A: Check out section 2.3.2 of this paper by Chapelle and Zien. They have a nice heuristic to select a good search range for $\sigma$ of the RBF kernel and $C$ for the SVM. I quote

To determine good values of the remaining free parameters (eg, by CV),
  it is important to search on the right scale. We therefore fix default
  values for $C$ and $\sigma$ that have the right order of magnitude. In
  a $c$-class problem we use the $1/c$ quantile of the pairwise
  distances $D^\rho_{ij}$ of all data-points as a default for $\sigma$.
  The default for $C$ is the inverses of the empirical variance $s^2$ in
  features space, which can be calculated by $s^2 = \frac{1}{n} \sum_i K_{ii} - \frac{1}{n^2}\sum_{i,j} K_{ij}$
   from a $n\times n$ kernel
  matrix $K$.

Afterwards, they use multiples (e.g. $2^k$ for $k\in \{-2,...,2\}$) of the default value as search range in a grid-search using cross-validation. That always worked very well for me.
Of course, we @ciri said, normalizing the data etc. is always a good idea.
