Stratified conjugate gradient boosting for fast training of conditional random fields
Bernd Gutmann, Kristian Kersting · Lirias · 2007
Boosting has recently been shown to be a promising approach for training conditional random fields (CRFs) as it allows to efficiently induce conjunctive (even relational) features. The potentials are represented as weighted sums of regression trees that are induced using gradient tree boosting. Its large scale application, however, suffers from two drawbacks: induced trees can spoil previous maximizations and the number of generated regression examples can become quite large. In this paper, we propose to tackle the latter problem by injecting randomness in the regression estimation procedure due to subsampling regression examples.