Dynamic Conditional Random Fields

@misc{citeulike:1005963, abstract = {Conditional random fields (CRFs) for sequence modeling have several advantages over joint models such as HMMs, including the ability to relax strong independence assumptions made in those models, and the ability to incorporate arbitrary overlapping features. Previous work has focused on linear-chain CRFs, which correspond to finite-state machines, and have efficient exact inference algorithms. Often, however, we wish to label sequence data in multiple interacting ways---for example,...}, author = {Labeling, For J. }, citeulike-article-id = {1005963}, keywords = {conditional, crfs, inference, markov, network}, posted-at = {2007-08-15 03:30:32}, priority = {0}, title = {Dynamic Conditional Random Fields}, url = {http://citeseer.ist.psu.edu/642639.html} }

TY - GEN ID - citeulike:1005963 L3 - citeulike-article-id:1005963 TI - Dynamic Conditional Random Fields N2 - Conditional random fields (CRFs) for sequence modeling have several advantages over joint models such as HMMs, including the ability to relax strong independence assumptions made in those models, and the ability to incorporate arbitrary overlapping features. Previous work has focused on linear-chain CRFs, which correspond to finite-state machines, and have efficient exact inference algorithms. Often, however, we wish to label sequence data in multiple interacting ways---for example,... KW - conditional KW - crfs KW - inference KW - markov KW - network AU - Labeling, For UR - http://citeseer.ist.psu.edu/642639.html ER -