title: Is transposing training set affects the results with SVM
body: <p>I am working on human age claffication where i have to classify data into two classes namely <code>Young</code> and <code>Old</code>. As a classifier i am using <code>SVM</code> and this is what i did so far to parepare the data : </p>
  
  <ol>
  <li>The <code>TrainingSet</code> where it's size is (11264, 284) : where each column corresponds to an <code>observation</code> (a person). This means that i have 284 persons for the training task and 11264 features.</li>
  <li>The <code>TestSet</code> is also formated as the <code>TrainingSet</code>.</li>
  <li>The <code>Groups</code> (labels) is a matrix Groups(284, 1) filled with (1) for <code>Olds</code> and (-1) for <code>Youngs</code>.</li>
  <li><p>I trained <code>SVM</code> using matlab built-in function to have the `SvmStruct'.</p>
  
  <pre><code>SvmStruct = svmtrain(TrainingSet, Groups')
  </code></pre></li>
  <li><p>Then i introduce the <code>TestSet</code> using this <code>matlab</code> function in order to have the classification results.</p>
  
  <pre><code>SvmClassify = svmclassify(SvmStruct, TestSet)
  </code></pre></li>
  </ol>
  
  <p>After i reviewed the <code>matlab</code> help about <code>SVM</code> i deduced that the data have to be introduced to the <code>SVM</code> classifier in a way that the each row of the <code>TrainingSet</code> corresponds to an <code>Observation</code> (a person in my case) and each column corresponds to a feature. So what i was doing so far was transposing those matrices (<code>TrainingSet</code> and <code>TestSet</code>). 
  Is what i did was wrong and all the results i got are wrong?</p>