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When quoting this document, please refer to the following
DOI: 10.4230/DagRep.1.7.53
URN: urn:nbn:de:0030-drops-33093
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2011/3309/
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Hein, Matthias ; Lugosi, Gabor ; Rosasco, Lorenzo ; Smale, Steve
Weitere Beteiligte (Hrsg. etc.): Matthias Hein and Gabor Lugosi and Lorenzo Rosasco and Steve Smale

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291)

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dagrep_v001_i007_p053_s11291.pdf (0.9 MB)

Abstract

The main goal of the seminar ``Mathematical and Computational Foundations of Learning Theory'' was to bring together experts from computer science, mathematics and statistics to discuss the state of the art in machine learning broadly construed and identify and formulate the key challenges in learning which have to be addressed in the future. This Dagstuhl seminar was one of the first meetings to cover the full broad range of facets of modern learning theory. The meeting was very successful and all participants agreed that such a meeting should take place on a regular basis.

BibTeX - Entry

@Article{hein_et_al:DR:2011:3309,
  author =	{Matthias Hein and Gabor Lugosi and Lorenzo Rosasco and Steve Smale},
  title =	{{Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291)}},
  pages =	{53--69},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{7},
  editor =	{Matthias Hein and Gabor Lugosi and Lorenzo Rosasco and Steve Smale},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2011/3309},
  URN =		{urn:nbn:de:0030-drops-33093},
  doi =		{10.4230/DagRep.1.7.53},
  annote =	{Keywords: learning theory, machine learning, sparsity, high-dimensional geometry, manifold learning, online learning}
}

Keywords: learning theory, machine learning, sparsity, high-dimensional geometry, manifold learning, online learning
Collection: Dagstuhl Reports, Volume 1, Issue 7
Issue Date: 2011
Date of publication: 25.11.2011

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