Due to learning in the similarity space with similarities as features, CBS learning generates a boundary based on similarities to separate the positive and negative training data in the similarity space, which is essentially a ball encompassing the positive training data in the original document space.
instead of explicitly minimizing the positively labeled open space risk, CBS learning approximates the radius !