Efficiently inducing features of conditional random fields Export

@inproceedings{citeulike:777827, abstract = {Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of CRFs is their great flexibility to include a wide variety of arbitrary, non-independent features of the input.}, author = {Mccallum, Andrew}, booktitle = {Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03)}, citeulike-article-id = {777827}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/mccallum03efficiently.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/mccallum03efficiently.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/mccallum03efficiently.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/mccallum03efficiently.html}, keywords = {crf, discriminative, feature-selection}, posted-at = {2008-01-17 18:23:37}, priority = {2}, title = {Efficiently inducing features of conditional random fields}, url = {http://citeseer.ist.psu.edu/mccallum03efficiently.html}, year = {2003} }

TY - CONF ID - citeulike:777827 L3 - citeulike-article-id:777827 N2 - Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of CRFs is their great flexibility to include a wide variety of arbitrary, non-independent features of the input. TI - Efficiently inducing features of conditional random fields T2 - Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03) KW - crf KW - discriminative KW - feature-selection AU - Mccallum, Andrew PY - 2003/// UR - http://citeseer.ist.psu.edu/mccallum03efficiently.html ER -