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IJCAI
2007

Training Conditional Random Fields Using Virtual Evidence Boosting

8 years 7 months ago
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.
Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A.
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz
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