Learning max-weight discriminative forests

We present a method for sequential learning of increasingly complex graphical models for discriminating between two hypotheses. We generate forests for each hypothesis, each with no more edges than a spanning tree, which optimize an information-theoretic criteria. The method relies on a straightforward extension of the efficient max-weight spanning tree (MWST) algorithm by incorporating multivalued edge-weights. Each iteration produces nested forests with increasing number of edges; each provably optimal as compared to alternative forests. Empirical results demonstrate superior probability of error as compared to generative approaches.