title: One vs all SVM introduces classses imbalance
body: <p>For one-against-all approach, it depends on binary classifiers equal to the number of classes n. It assumes that a class is labeled as one while the rest of classes are labeled 0:</p>
  
  <pre><code>model = cell(numLabels,1);
  for k=1:numLabels
      model{k} = svmtrain(double(trainLabel==k), trainData, '-c 1 -g 0.2 -b 1');
  end
  </code></pre>
  
  <p>For a large number of classes and large number of images(5000 each class). This means in the the above code one class will be 5000 vs the rest of the dataset which (n-1)*5000. Is this imbalance acceptable?. I mean one vs one would be better to avoid imbalance or is it dependent on classification problem?is it acceptable to have such situation?How to know if that is causing me a problem?</p>