Learning theory and approximation. Abstracts from the workshop held July 3--9, 2016
type: Item
instance of: scholarly article
title: Learning theory and approximation. Abstracts from the workshop held July 3--9, 2016
zbMATH Open document ID: 1380.00052
DOI: 10.4171/OWR/2016/33
published in: Oberwolfach Reports
publication date: 2018-02-21T00:00:00Z
review text: Summary: The main goal of this workshop -- the third one of this type at the MFO -- has been to blend mathematical results from statistical learning theory and approximation theory to strengthen both disciplines and use synergistic effects to work on current research questions. Learning theory aims at modeling unknown function relations and data structures from samples in an automatic manner. Approximation theory is naturally used for the advancement and closely connected to the further development of learning theory, in particular for the exploration of new useful algorithms, and for the theoretical understanding of existing methods. Conversely, the study of learning theory also gives rise to interesting theoretical problems for approximation theory such as the approximation and sparse representation of functions or the construction of rich kernel reproducing Hilbert spaces on general metric spaces. This workshop has concentrated on the following recent topics: Pitchfork bifurcation of dynamical systems arising from mathematical foundations of cell development; regularized kernel based learning in the Big Data situation; deep learning; convergence rates of learning and online learning algorithms; numerical refinement algorithms to learning; statistical robustness of regularized kernel based learning.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 41A35
Mathematics Subject Classification ID: 41A63
Mathematics Subject Classification ID: 62Gxx
Mathematics Subject Classification ID: 68-06
Mathematics Subject Classification ID: 62-06
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62Hxx
zbMATH DE Number: 6841698
MaRDI profile type: Publication
Wikidata QID: Q57340885
description: scientific article; zbMATH DE number 6841698