Accelerated training of conditional random fields with stochastic gradient methods

S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy
2006 Proceedings of the 23rd international conference on Machine learning - ICML '06  
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques.
doi:10.1145/1143844.1143966 dblp:conf/icml/VishwanathanSSM06 fatcat:jcailkx4ufar3f5v23vfqpxbfq