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  • École d’Été de Probabilités de Saint-Flour XXXVIII-2008
    Vladimir Koltchinskii
    978-3-642-22147-7
    2011
    Edition 1
    • Provides a unified framework for machine learning problems (such as large margin
    • classification), sparse recovery and low rank matrix problems
    • Develops a variety of probabilistic inequalities for empirical processes needed to obtain error bounds
    • in machine learning and sparse recovery
    • Develops a comprehensive theory of excess risk bounds and oracle inequalities for penalized empirical
    • risk minimization
    • Includes supplementary material: sn.pub/extras

    €150

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