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ICML
2004
IEEE

Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs

11 years 10 months ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated with polynomial time complexity. This is in contrast to existing labelers that can handle only features of a small number of hidden variables, such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees, and graphs and efficient algorithms for them.
Hisashi Kashima, Yuta Tsuboi
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where ICML
Authors Hisashi Kashima, Yuta Tsuboi
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