Training Conditional Random Fields Using Virtual Evidence Boosting

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 simultaneously performs feature selection and parameter estimation. To achieve this, we extend standard boosting to handle virtual evidence, where an observation 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 classification problems, our new training algorithm outperforms other training approaches including maximum likelihood, maximum pseudo-likelihood, and the most recent boosted random fields.

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz
• Keywords: Artificial Intelligence, Conditional Random fields, IJCAI 2007, Random fields, Virtual Evidence