Is it better to have more training data which are close to the decision boundary, or more data which are "typical" of their class? • /r/MachineLearning

I'm developing a program which uses a multi step classification process, with the idea being that after an initial classification is done, a new set of pixels are chosen by the program to be classed as training data for another iteration. I'm trying to figure out which classifiers do better with training data which is closer to the decision boundary, and which do better with training data which is more typical of the class it represents