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Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
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Authors: Mario Marchand,Hongyu Su,Emilie Morvant,Juho Rousu,John S. Shawe-Taylor
Where published: NeurIPS 2014 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/5382-multilabel-structured-output-learning-with-random-spanning-trees-of-max-margin-markov-networks
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable inference. The experimental results confirm that practical learning is scalable to realistic datasets using this approach.
Where published: NeurIPS 2014 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/5382-multilabel-structured-output-learning-with-random-spanning-trees-of-max-margin-markov-networks
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable inference. The experimental results confirm that practical learning is scalable to realistic datasets using this approach.
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