Several instance-based large-margin classifiers have recently been put forward in the literature: Support Hyperplanes, Nearest Convex Hull classifier and Soft Nearest Neighbor. We examine those techniques from a common fit-versuscomplexity framework and study the links between them. Finally, we compare the performance of these techniques vis-a-vis each other and other standard classification methods.