Full-Text PDF (507 KB) | Introduction as PDF | Metadata | Table of Contents | OWR summary
Published online: 2013-02-20
Learning Theory and ApproximationKurt Jetter, Steve Smale and Ding-Xuan Zhou (1) Universität Hohenheim, Stuttgart, Germany
(2) City University of Hong Kong, China
(3) City University of Hong Kong, China
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.
No keywords available for this article.
Jetter Kurt, Smale Steve, Zhou Ding-Xuan: Learning Theory and Approximation. Oberwolfach Rep. 9 (2012), 1895-1948. doi: 10.4171/OWR/2012/31