Uniform approximation of functions with random bases 论文

摘要

Random networks of nonlinear functions have a long history of empirical success in function fitting but few theoretical guarantees. In this paper, using techniques from probability on Banach Spaces, we analyze a specific architecture of random nonlinearities, provide L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> 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.