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Documents authored by Siebes, Arno


Document
07181 Abstracts Collection – Parallel Universes and Local Patterns

Authors: Michael R. Berthold, Katharina Morik, and Arno Siebes

Published in: Dagstuhl Seminar Proceedings, Volume 7181, Parallel Universes and Local Patterns (2007)


Abstract
From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel Universes and Local Patterns'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cite as

Michael R. Berthold, Katharina Morik, and Arno Siebes. 07181 Abstracts Collection – Parallel Universes and Local Patterns. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@InProceedings{berthold_et_al:DagSemProc.07181.1,
  author =	{Berthold, Michael R. and Morik, Katharina and Siebes, Arno},
  title =	{{07181 Abstracts Collection – Parallel Universes and Local Patterns }},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.1},
  URN =		{urn:nbn:de:0030-drops-12662},
  doi =		{10.4230/DagSemProc.07181.1},
  annote =	{Keywords: Local Patterns, Global Models, Parallel Universes, Descriptor Spaces}
}
Document
07181 Introduction – Parallel Universes and Local Patterns

Authors: Michael R. Berthold, Katharina Morik, and Arno Siebes

Published in: Dagstuhl Seminar Proceedings, Volume 7181, Parallel Universes and Local Patterns (2007)


Abstract
Learning in parallel universes and the mining for local patterns are both relatively new fields of research. Local pattern detection addresses the problem of identifying (small) deviations from an overall distribution of some underlying data in some feature space. Learning in parallel universes on the other hand, deals with the analysis of objects, which are given in different feature spaces, i.e.\ parallel universes; and the aim is on finding groups of objects, which show ``interesting'' behavior in some of these universes. So, while local patterns describe interesting properties of a subset of the overall space or set of objects, learning in parallel universes also aims at finding interesting patterns across different feature spaces or object descriptions. Dagstuhl Seminar~07181 on Parallel Universes and Local Patterns held in May 2007 brought together researchers with different backgrounds to discuss latest advances in both fields and to draw connections between the two.

Cite as

Michael R. Berthold, Katharina Morik, and Arno Siebes. 07181 Introduction – Parallel Universes and Local Patterns. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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@InProceedings{berthold_et_al:DagSemProc.07181.2,
  author =	{Berthold, Michael R. and Morik, Katharina and Siebes, Arno},
  title =	{{07181 Introduction – Parallel Universes and Local Patterns }},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.2},
  URN =		{urn:nbn:de:0030-drops-12655},
  doi =		{10.4230/DagSemProc.07181.2},
  annote =	{Keywords: Local Patterns, Global Models, Parallel Universes, Descriptor Spaces}
}
Document
04161 Abstracts Collection – Detecting Local Patterns

Authors: Jean-François Boulicaut, Katharina Morik, and Arno Siebes

Published in: Dagstuhl Seminar Proceedings, Volume 4161, Detecting Local Patterns (2006)


Abstract
From 12.04.04 to 16.04.04, the Dagstuhl Seminar 04161 ``Detecting Local Patterns'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cite as

Jean-François Boulicaut, Katharina Morik, and Arno Siebes. 04161 Abstracts Collection – Detecting Local Patterns. In Detecting Local Patterns. Dagstuhl Seminar Proceedings, Volume 4161, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


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@InProceedings{boulicaut_et_al:DagSemProc.04161.1,
  author =	{Boulicaut, Jean-Fran\c{c}ois and Morik, Katharina and Siebes, Arno},
  title =	{{04161 Abstracts Collection – Detecting Local Patterns}},
  booktitle =	{Detecting Local Patterns},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{4161},
  editor =	{Jean-Fran\{\backslashc\{c\}\}ois Boulicaut and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04161.1},
  URN =		{urn:nbn:de:0030-drops-4677},
  doi =		{10.4230/DagSemProc.04161.1},
  annote =	{Keywords: }
}
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