This work relates to the implementation of a 2D conditional random field model in the context of document image analysis. Our model makes it possible to take variability into acco...
We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...
Daniel J. Lizotte, Michael H. Bowling, Susan A. Mu...
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A que...
In recent years, extraction of temporal relations for events that express sentiments has drawn great attention of the Natural Language Processing (NLP) research communities. In thi...