COMPLEXITY REGULARIZATION VIA LOCALIZED RANDOM PENALTIES By

Abstract

In this article, model selection via penalized empirical loss minimization in nonparametric classification problems is studied. Data-dependent penalties are constructed, which are based on estimates of the complexity of a small subclass of each model class, containing only those functions with small empirical loss. The penalties are novel since those… (More)

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