On Generalizing the C-Bound to the Multiclass and Multi-label Settings

Laviolette, Francois, Morvant, Emilie, Ralaivola, Liva, Roy, Jean-Francis

arXiv.org Machine Learning 

The C-bound, introduced in Lacasse et al. [1], gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.

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