Training Conditional Random Fields using Virtual Evidence Boosting

<p>While conditional random fields (CRFs) have been applied successfully in a variety of domains, their training remains a challenging task. In this paper, we introduce a novel training method for CRFs, called virtual evidence boosting, which simulta- neously performs feature selection and parameter estimation. To achieve this, we extend standard boosting to handle virtual evidence, where an ob- servation can be specified as a distribution rather than a single number. This extension allows us to develop a unified framework for learning both local and compatibility features in CRFs. In experiments on synthetic data as well as real activity classifi- cation problems, our new training algorithm out- performs other training approaches including max- imum likelihood, maximum pseudo-likelihood, and the most recent boosted random fields.</p>

http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/veb-ijcai-07... PDF