Web: http://arxiv.org/abs/2201.06487

Sept. 15, 2022, 1:11 a.m. | Santiago Mazuelas, Mauricio Romero, Peter Grünwald

cs.LG updates on arXiv.org arxiv.org

Supervised classification techniques use training samples to learn a
classification rule with small expected 0-1 loss (error probability).
Conventional methods enable tractable learning and provide out-of-sample
generalization by using surrogate losses instead of the 0-1 loss and
considering specific families of rules (hypothesis classes). This paper
presents minimax risk classifiers (MRCs) that minimize the worst-case 0-1 loss
over general classification rules and provide tight performance guarantees at
learning. We show that MRCs are strongly universally consistent using feature
mappings given …

arxiv classifiers loss minimax risk

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