Multilabel structured output learning with random spanning trees of max-margin Markov networks

M. Marchand, S. Hongyu, E. Morvant, J. Rousu, J. Shawe Taylor, in:, Neural Information Processing Systems, 2014.

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Abstract
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.
Publishing Year
Date Published
2014-01-01
Conference
NIPS: Neural Information Processing Systems
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Cite this

Marchand M, Hongyu S, Morvant E, Rousu J, Shawe Taylor J. Multilabel structured output learning with random spanning trees of max-margin Markov networks. In: Neural Information Processing Systems; 2014.
Marchand, M., Hongyu, S., Morvant, E., Rousu, J., & Shawe Taylor, J. (2014). Multilabel structured output learning with random spanning trees of max-margin Markov networks. Presented at the NIPS: Neural Information Processing Systems, Neural Information Processing Systems.
Marchand, Mario, Su Hongyu, Emilie Morvant, Juho Rousu, and John Shawe Taylor. “Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.” Neural Information Processing Systems, 2014.
M. Marchand, S. Hongyu, E. Morvant, J. Rousu, and J. Shawe Taylor, “Multilabel structured output learning with random spanning trees of max-margin Markov networks,” presented at the NIPS: Neural Information Processing Systems, 2014.
Marchand M, Hongyu S, Morvant E, Rousu J, Shawe Taylor J. 2014. Multilabel structured output learning with random spanning trees of max-margin Markov networks. NIPS: Neural Information Processing Systems
Marchand, Mario, et al. Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. Neural Information Processing Systems, 2014.

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