Kernel conditional random fields

John Lafferty, Xiaojin Zhu, Yan Liu
2004 Twenty-first international conference on Machine learning - ICML '04  
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature
more » ... ces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
doi:10.1145/1015330.1015337 dblp:conf/icml/LaffertyZL04 fatcat:uhrihq6tkfgyviat42mbcxl27e