Concept classes can canonically be represented by sign-matrices, i.e., by matrices with entries 1 and −1. The question whether a sign-matrix (concept class) A can be learned by a machine that performs large margin classification is closely related to the "margin complexity" associated with A. We consider several variants of margin complexity, reveal how they are related to each other, and we reveal how they are related to other notions of learning-theoretic relevance like SQ-dimension, CSQ-dimension, and the Forster bound.