SMO Lattices for the Parallel Training of Support Vector Machines

In this work, a method is proposed to train Support Vector Machines in parallel. The dierence to other parallel implementations is that the problem is decomposed into hierarchically connected nodes and that each node does not have to fully optimize its local problem. Instead Lagrange multipliers are ltered and transferred between nodes during runtime, with important ones ascending and unimportant ones descending inside the architecture. Experimental validation demonstrates the advan- tages in terms of speed in comparison to other approaches.