Training Conditional Random Fields Using Virtual Evidence Boosting

Abstract

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… (More)

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