The goal of our learning problem is to find w ∈ R d which minimizes the expected loss, aka risk, over a new sample D of size N : Generally, the loss function ∆ cannot be decomposed into a linear combination of a loss function δ over individual training samples.
most discriminative large-margin learning algorithms assume for simplicity that the loss function is decomposable and the samples are i.i.d.