Support vector machines do binary classification. If there is more than two classes, it is possible to train several classifiers instead of one. Two common approaches are training one vs. one (each class against each other class) and one vs. all classifiers (each class against all other classes).
Say that I have relatively small sample size, e.g. N=200. Do I split the data into training and test sets separately for each classifier?