infin and L₂ error bounds for approximating functions in Reproducing Kernel Hilbert Spaces, and discuss scenarios when these expansions are dense in the continuous functions. We discuss connections between these random nonlinear networks and popular machine learning algorithms and show experimentally that these networks provide competitive performance at far lower computational cost on large-scale pattern recognition tasks.">

Uniform approximation of functions with random bases (original) (raw)

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