Note that this avoids inspecting many of the possible cluster combinations: once a cluster is built (e.g., during the previous iterations or by the deterministic sieves in step 8), we do not generate training data from its members, but rather treat it as an atomic unit.
our approach generates more training data than online learning, which trains using only the actual decisions taken during inference in each iteration (i.e., the pair (e 1 , e 2 ) in step 13).