Approximate Parameter Learning in Discriminative Fields

Abstract

In this paper, we present an approach for approximate maximum likelihood parameter learning in discriminative field models, which is based on approximating true expectations with simple piecewise constant functions constructed using inference techniques. Gradient ascent with these updates shows interesting weak-convergence behavior which is tied closely to… (More)

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