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arXiv · 1408.1336

On the Generalization of the C-Bound to Structured Output Ensemble Methods

François Laviolette·Emilie Morvant·Liva Ralaivola·Jean-Francis Roy
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Abstract

This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the \Cbound, an upper bound over the risk of models expressed as a weighted majority vote that is based on the first and second statistical moments of the vote's margin. This bound may advantageously $(i)$ be applied on more complex outputs such as multiclass labels and multilabel, and $(ii)$ allow to consider margin relaxations. These results open the way to develop new ensemble methods for structured output prediction with PAC-Bayesian guarantees.

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François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy. 2014-08-06. On the Generalization of the C-Bound to Structured Output Ensemble Methods. https://arxiv.org/abs/1408.1336

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