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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 featuredoi:10.1145/1015330.1015337 dblp:conf/icml/LaffertyZL04 fatcat:uhrihq6tkfgyviat42mbcxl27e