title: What is the relation between the number of Support Vectors and training data and classifiers performance?
body: <p>I am using LibSVM to classify some documents. The documents seem to be a bit difficult to classify as the final results show. However, I have noticed something while training my models. and that is: If my training set is for example 1000 around 800 of them are selected as support vectors.
  I have looked everywhere to find if this is a good thing or bad. I mean is there a relation between the number of support vectors and the classifiers performance?
  I have read this <a href="https://stackoverflow.com/questions/5731169/svm-all-my-training-data-is-getting-selected-as-support-vectors-why">previous post</a> but I am performing a parameter selection and also I am sure that the attributes in the feature vectors are all ordered.
  I just need to know the relation.
  Thanks.
  p.s: I use a linear kernel.</p>