title: Applying trained SVM to completely new data, retrieving labels
body: <p>Say I have a simple implementation for SVM-classification as for instance:</p>
  
  <pre><code>%# train an SVM model over training instances    
  svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ...       
               'Autoscale',true, 'Showplot',false, 'Method','QP', ...        
               'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);   
  
  %# test using test instances  
  pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false);  
  
  %# evaluate and update performance object
  cp = classperf(cp, pred, testIdx);
  </code></pre>
  
  <p>Now I want the following: Train the classifier on one dataset, where I have labels, and then apply it to another dataset from which I do not know the labels at all. Is this possible? If so, would I use the line where the prediction ("pred") is made to classify the unlabelled data (or alternatively optimize parameters based on my labeled data and use the best model's prediction on the test data)?</p>