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Abstract: Kernel conditional random elds 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 elds arise from risk minimization procedures de ned 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 spaces into... (Update)
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J. Lafferty, X. Zhu, and Y. Liu. Kernel conditional random fields: representation and clique selection. In Proc. of the Int. Conference on Machine Learning, 2004. http://citeseer.ist.psu.edu/article/lafferty04kernel.html More
@misc{ lafferty04kernel,
author = "J. Lafferty and X. Zhu and Y. Liu",
title = "Kernel conditional random fields: representation and clique selection",
text = "J. Lafferty, X. Zhu, and Y. Liu. Kernel conditional random fields: representation
and clique selection. In Proc. of the Int. Conference on Machine Learning,
2004.",
year = "2004",
url = "citeseer.ist.psu.edu/article/lafferty04kernel.html" }
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