learning biases. In this paper we bring them into a unied view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view which helps us uncover the relations of the two algorithms. We show that LMNN can be seen as learning a set of local SVM-like models in a quadratic space. Along the way and inspired by the metric-based interpretation of SVMs we derive a novel variant of SVMs, -SVM, to which LMNN is even more similar. We give a unied view of LMNN and the dierent SVM variants. Finally we provide some preliminary experiments on a number of benchmark datasets in which show that -SVM compares favorably both with respect to LMNN and SVM.