title: Select optimized parameters for libsvm-linear kernel
body: <p>I use <code>RBF</code> kernel for ML with libsvm. I am looking into exploring other kernels for my dataset. </p>
  
  <p>There are multiple parameters to optimize each for specific kernel. <code>C and g</code> parameters are used for grid search for selecting optimal combination of Cost and gamma.</p>
  
  <pre><code>-d degree : set degree in kernel function (default 3)
  -g gamma : set gamma in kernel function (default 1/num_features)
  -r coef0 : set coef0 in kernel function (default 0)
  -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
  -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
  -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
  -m cachesize : set cache memory size in MB (default 100)
  -e epsilon : set tolerance of termination criterion (default 0.001)
  -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
  -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
  -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
  </code></pre>
  
  <p>I want to know relevant  parameters for each individual kernel. Since there are multiple parameters for selection. Ex: <code>C and g</code> for <code>RBF</code> kernel. Also provide grid size and range of parameters Ex: <code>10^-3 to 10^11 for C and 10^3 to 10^-13 for g</code></p>
  
  <p>My perl grid generator for RBF kernel:</p>
  
  <pre><code>    for ( $i = -3; $i &lt;= 11; $i += 1 ) {
  
      for ( $j = 3; $j &gt;= -13; $j += -1 ) {
  
          my $a = 2**$i;
          my $b = 2**$j;
  
          $output = "svm-train -c $a -g $b -v 5 $ARGV[0]";
  
          print  "$output &gt;&amp; ${ARGV[0]}_${a}_${b}.out \n";
  
      }
  }
  </code></pre>