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Accelerated training of conditional random fields with stochastic gradient methods

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dc.contributor.authorVishwanathan, S
dc.contributor.authorSchraudolph, Nicol
dc.contributor.authorSchmidt, Mark W.
dc.contributor.authorMurphy, Keven P.
dc.coverage.spatialPittsburgh USA
dc.date.accessioned2015-12-07T22:38:49Z
dc.date.createdJune 25-29 2006
dc.date.issued2006
dc.date.updated2015-12-07T10:41:57Z
dc.description.abstractWe 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.
dc.identifier.isbn1595933832
dc.identifier.urihttp://hdl.handle.net/1885/23596
dc.publisherAssociation for Computing Machinery Inc (ACM)
dc.relation.ispartofseriesInternational Conference on Machine Learning (ICML 2006)
dc.sourceProceedings of 23rd International Conference of Machine Learning
dc.source.urihttp://shop.omnipress.com/icml/toc.pdf
dc.subjectKeywords: Convergence of numerical methods; Data reduction; Learning systems; Optimization; Conditional Random Fields (CRF); Data sets; Stochastic gradient optimization; Stochastic Meta Descent (SMD); Random processes
dc.titleAccelerated training of conditional random fields with stochastic gradient methods
dc.typeConference paper
local.bibliographicCitation.lastpage976
local.bibliographicCitation.startpage969
local.contributor.affiliationVishwanathan, S, College of Engineering and Computer Science, ANU
local.contributor.affiliationSchraudolph, Nicol, College of Engineering and Computer Science, ANU
local.contributor.affiliationSchmidt, Mark W., University of British Columbia
local.contributor.affiliationMurphy, Keven P., University of British Columbia
local.contributor.authoruidVishwanathan, S, a204054
local.contributor.authoruidSchraudolph, Nicol, a205905
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB27
local.identifier.scopusID2-s2.0-33749243756
local.type.statusPublished Version

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