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DOI: 10.4230/DagRep.1.8.67
URN: urn:nbn:de:0030-drops-33125
URL: http://drops.dagstuhl.de/opus/volltexte/2011/3312/

Biehl, Michael ; Hammer, Barbara ; Merényi, Erzsébet ; Sperduti, Alessandro ; Villman, Thomas
Weitere Beteiligte (Hrsg. etc.): Michael Biehl and Barbara Hammer and Erzsébet Merényi and Alessandro Sperduti and Thomas Villmann

Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)

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Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 11341 "Learning in the context of very high dimensional data". The aim of the seminar was to bring together researchers who develop, investigate, or apply machine learning methods for very high dimensional data to advance this important field of research. The focus was be on broadly applicable methods and processing pipelines, which offer efficient solutions for high-dimensional data analysis appropriate for a wide range of application scenarios.

BibTeX - Entry

@Article{biehl_et_al:DR:2011:3312,
  author =	{Michael Biehl and Barbara Hammer and Erzs{\'e}bet Mer{\'e}nyi and Alessandro Sperduti and Thomas Villman},
  title =	{{Learning in the context of very high dimensional data (Dagstuhl Seminar 11341)}},
  pages =	{67--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{8},
  editor =	{Michael Biehl and Barbara Hammer and Erzs{\'e}bet Mer{\'e}nyi and  Alessandro Sperduti and Thomas Villmann},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2011/3312},
  URN =		{urn:nbn:de:0030-drops-33125},
  doi =		{http://dx.doi.org/10.4230/DagRep.1.8.67},
  annote =	{Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization}
}

Keywords: Curse of dimensionality, Dimensionality reduction, Regularization Deep learning, Visualization
Seminar: Dagstuhl Reports, Volume 1, Issue 8
Issue date: 2011
Date of publication: 06.12.2011


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