title: Train cascade classifier
body: <p>I got some questions about the training of a cascade classifier:</p>
  
  <ol>
  <li>On Some of my pictures half of the object is visible. Should I mark the visible part as region of interest, use the picture as negative sample or sort it out completely?</li>
  <li>Is the classifier able to detect objects that are just partly visible (using Haar features)?</li>
  <li>What should be the ratio of negative and positive samples? Often I read that you should use more negative samples. But for example in <a href="https://stackoverflow.com/questions/16058080/how-to-train-cascade-properly">this</a> thread it is mentioned that the ratio should be 2:1 (more positive samples).</li>
  <li>My current classifier detects to much false positives. According to <a href="http://de.mathworks.com/help/vision/ug/train-a-cascade-object-detector.html" rel="nofollow noreferrer">this</a> tutorial you can either increase the number of stages or decrease the false alarm rate per stage. But I can't increase the number of stages without increasing the false alarm rate. If I just increase the number of stages, the training stops at some point because the classifier runs out of samples. Is the only way to reduce the false positives to increase the number of samples?</li>
  </ol>
  
  <p>Would be glad if someone could answer one of my questions :)</p>