Approximate Parameter Learning in Discriminative Fields
In this paper, we present an approach for approximate maximu m likelihood parameter learning in discriminative field models, whi ch is based on approximating true expectations with simple piecewise c onstant functions constructed using inference techniques. Gradient as cent with these updates shows interesting weak-convergence behavio r which is tied closely to the number of errors made during inference. The pe rformance of various approximations was evaluated with different inf erence techniques showing that the learned parameters lead to good clas sific tion performance so long as the method used for approximating the radient is consistent with the inference mechanism. The proposed ap pro ch is general enough to be used for conditional training of conven tional MRFs.