file.pdf (293.97 kB)
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
journal contribution
posted on 2005-01-01, 00:00 authored by Sanjiv Kumar, Jonas August, Martial HebertEstimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for ap-
proximate 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 exhibits compelling limit cycle 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 the training of, e.g., smoothing
parameters of conventional Markov Random Fields (MRFs).