Accelerated training of conditional random fields with stochastic gradient methods Export

@inproceedings{citeulike:4829353, abstract = {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.}, address = {New York, NY, USA}, author = {Vishwanathan, S. V. N. and Schraudolph, Nicol N. and Schmidt, Mark W. and Murphy, Kevin P.}, booktitle = {ICML '06: Proceedings of the 23rd international conference on Machine learning}, citeulike-article-id = {4829353}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1143966}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1143844.1143966}, doi = {10.1145/1143844.1143966}, isbn = {1-59593-383-2}, keywords = {conditional, crf, fields, random, sgd, training}, location = {Pittsburgh, Pennsylvania}, pages = {969--976}, posted-at = {2009-10-12 09:08:47}, priority = {2}, publisher = {ACM}, title = {Accelerated training of conditional random fields with stochastic gradient methods}, url = {http://dx.doi.org/10.1145/1143844.1143966}, year = {2006} }

TY - CONF ID - citeulike:4829353 L3 - citeulike-article-id:4829353 N2 - 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. CY - Pittsburgh, Pennsylvania SN - 1-59593-383-2 AD - New York, NY, USA EP - 976 TI - Accelerated training of conditional random fields with stochastic gradient methods PB - ACM T2 - ICML '06: Proceedings of the 23rd international conference on Machine learning SP - 969 KW - conditional KW - crf KW - fields KW - random KW - sgd KW - training AU - Vishwanathan, SVN AU - Schraudolph, Nicol AU - Schmidt, Mark AU - Murphy, Kevin PY - 2006/// UR - http://dx.doi.org/10.1145/1143844.1143966 ER -