JournalsowrVol. 9 / No. 2DOI 10.4171/owr/2012/31

Learning Theory and Approximation

  • Kurt Jetter

    Universität Hohenheim, Stuttgart, Germany
  • Steve Smale

    City University of Hong Kong, China
  • Ding-Xuan Zhou

    City University of Hong Kong, China
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Abstract

Learning theory studies data structures from samples and aims at understanding unknown function relations behind them. This leads to interesting theoretical problems which can be often attacked with methods from Approximation Theory. This workshop - the second one of this type at the MFO - has concentrated on the following recent topics: Learning of manifolds and the geometry of data; sparsity and dimension reduction; error analysis and algorithmic aspects, including kernel based methods for regression and classification; application of multiscale aspects and of refinement algorithms to learning.