BibTeX
@MISC{Fields_approximateparameter,
author = {Discriminative Fields},
title = {Approximate Parameter Learning in},
year = {}
}
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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 the number of errors made during inference. The performance of various approximations was evaluated with different inference techniques showing that the learned parameters lead to good classification performance so long as the method used for approximating the gradient is consistent with the inference mechanism. The proposed approach is general enough to be used for conditional training of conventional MRFs. 1
Keyphrases
approximate parameter learning inference technique learned parameter different inference technique discriminative field model weak-convergence behavior true expectation simple piecewise constant function inference mechanism approximate maximum likelihood parameter gradient ascent conventional mrfs various approximation good classification performance conditional training
