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Abstract:
We present an algorithm that induces a class of models with
thin junction trees
-- models that are characterized by an upper bound on the size
of the maximal cliques of their triangulated graph. By ensuring
that the junction tree is thin, inference in our models remains
tractable throughout the learning process. This allows both an
efficient implementation of an iterative scaling parameter
estimation algorithm and also ensures that inference can be
performed efficiently with the final model. We illustrate the
approach with applications in handwritten digit recognition and
DNA splice site detection.
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