Statistical learning by sparse deep neural networks
Abstract
We consider a deep neural network estimator based on empirical risk minimization with l1-regularization. We derive a general bound for its excess risk in regression and classification (including multiclass), and prove that it is adaptively nearly-minimax (up to log-factors) simultaneously across the entire range of various function classes.
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