title: Techniques to improve the accuracy of SVM classifier
body: <p>I am trying to build a classifier to predict breast cancer using the UCI dataset. I am using support vector machines. Despite my most sincere efforts to improve upon the accuracy of the classifier, I cannot get beyond 97.062%. I've tried the following:</p>
  
  <pre><code>1. Finding the most optimal C and gamma using grid search.
  2. Finding the most discriminative feature using F-score.
  </code></pre>
  
  <p>Can someone suggest me techniques to improve upon the accuracy? I am aiming at at least 99%. </p>
  
  <pre><code>1.Data are already normalized to the ranger of [0,10]. Will normalizing it to [0,1]  help?
  
  2. Some other method to find the best C and gamma?
  </code></pre>