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Learning max-weight discriminative forests
Tan, V.Y.F.; Fisher, J.W.; Willsky, A.S.;
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
March 31 2008-April 4 2008
Page(s):1877
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1880
Abstract:
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.
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