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arXiv · 1805.09717

Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms

Mathieu Blondel·André F. T. Martins·Vlad Niculae
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Abstract

This paper studies Fenchel-Young losses, a generic way to construct convex loss functions from a regularization function. We analyze their properties in depth, showing that they unify many well-known loss functions and allow to create useful new ones easily. Fenchel-Young losses constructed from a generalized entropy, including the Shannon and Tsallis entropies, induce predictive probability distributions. We formulate conditions for a generalized entropy to yield losses with a separation margin, and probability distributions with sparse support. Finally, we derive efficient algorithms, making Fenchel-Young losses appealing both in theory and practice.

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BibTeXRIS

Mathieu Blondel, André F. T. Martins, Vlad Niculae. 2018-05-24. Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. https://arxiv.org/abs/1805.09717

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