Tight risk bounds for multi-class margin classifiers

Yu Maximov, D. Reshetova

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    We consider a problem of risk estimation for large-margin multi-class classifiers. We propose a novel risk bound for the multi-class classification problem. The bound involves the marginal distribution of the classifier and the Rademacher complexity of the hypothesis class. We prove that our bound is tight in the number of classes. Finally, we compare our bound with the related ones and provide a simplified version of the bound for the multi-class classification with kernel based hypotheses.

    Original languageEnglish
    Pages (from-to)673-680
    Number of pages8
    JournalPattern Recognition and Image Analysis
    Issue number4
    Publication statusPublished - 1 Oct 2016


    • excess risk bound
    • multi-class classification
    • statistical learning


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